7RROLN )LHOG6WDWLRQ$. (IIHFWVRIZLQWHUSUHFLSLWDWLRQRQFDUERQG\QDPLFVLQ$UFWLFWXQGUD (OHQD%ODQF%HWHV%HQMDPLQ07KXUQKRIIHU0LTXHO $*RQ]DOH]0HOHU1HLO&6WXUFKLR-HIIUH\0:HONHU 8QLYHUVLW\RI,OOLQRLVDW&KLFDR,/86$)XOEULJKW'LVWLQJXLVKHG86$UFWLF&KDLU1RUZD\8QLYHUVLW\&HQWHULQ6YDOEDUG/RQJ\HDUE\HQ6YDOEDUG1RUZD\

R¶1R¶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

  Å 

D D E   )OX[ PJ&+ FKDQJHVLQZLQWHUVQRZDFFXPXODWLRQKDYHEHHQDVVRFLDWHGZLWK  D  E 

D &&2

YHJHWDWLRQVKLIWVIDYRULQJHFRV\VWHPSURGXFWLYLW\DVVRFLDWHG 7KDZ'HSWK FP D %E 

 E G D  DOWHUDWLRQVLQWKHUPDODQGK\GURORJLFDOUHJLPHVPD\SURPSWVKLIWVLQ D D F E   D  DEF DE DD D D D D D $D   $D   WKHGHFRPSRVLWLRQSDWKZD\VWULJJHULQJ&+ HPLVVLRQV%HFDXVH       (FRV\VWHP&+ F  D  LVDSDUWLFXODUO\SRWHQW*+* &2 HTXLYDOHQWVRYHU\HDU   D F &+  F

G  -XQ -XQ -XO -XO $XJE $XJG *6F

E  F 0HDQ E 

VFHQDULRV HQKDQFHGHPLVVLRQVRIWKLVJDVFDQUHVXOWLQQHJDWLYH E V E E E

D  D D %E  D         D F D E

P D

IHHGEDFNVRQFOLPDWHV\VWHP&HIIOX[DVPXFKDVWKHIRUPRI&     D $D F D D G &&+ Å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“ VWDQGDUGHUURU 0HDQYDOXHVZLWKLQDWLPHSHULRG HFRV\VWHP&+ IOX[HV  F E ZLWKGLIIHUHQWORZHUFDVHOHWWHUGHQRWHVWDWLVWLFDOGLIIHUHQFHV $129$ D E  ([DPLQHSRWHQWLDOVKLIWVLQWKHGHFRPSRVLWLRQSDWKZD\VFRQWULEXWLQJWR D   %E S  EHWZHHQVQRZFRYHUWUHDWPHQWV3RVLWLYHQHWHFRV\VWHP&+ WKHILQDO&IOX[EDODQFH E D D IOX[HVGHQRWH&ORVVHVWRWKHDWPRVSKHUH1HJDWLYHQHWHFRV\VWHP&+ $D   $D IOX[HVGHQRWH&XSWDNHIURPWKHDWPRVSKHUH6WDWLVWLFDOVLJQLILFDQFH   ,QYHVWLJDWHWKHIDFWRUVUHJXODWLQJVRLO&2 DQGHFRV\VWHP&+ IOX[HV D

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“  F &DUERQHIIOX[DQGWKHIRUPRI&HPLWWHG &2 RU&+ UHVSRQGHGGLIIHUHQWO\WRWKHVDPHYDULDEOHV7KHUHIRUHLQFOXGLQJWKHVH G&&+     ,QWHUPHGLDWH'ULIW “    PHFKDQLVPVLQPRGHOVSUHGLFWLQJ&IHHGEDFNVIURPSHUPDIURVWDIIHFWHGDUHDVWRWKHJOREDOFOLPDWHV\VWHPLV :KHUHDF LVWKHFDUERQLVRWRSLFIUDFWLRQDWLRQEHWZHHQ&+ DQG&2DQGG &&2 DQGG &&+ UHIHU WRWKHFDUERQLVRWRSLFFRPSRVLWLRQRI&2 DQG&+UHVSHFWLYHO\ 7DEOH5DGRQVXUIDFHHPLVVLRQVIURP&RQWURODQG,QWHUPHGLDWH6QRZ'ULIWVLWHV PHDQ IXQGDPHQWDO $OWHUDWLRQLQVRLOGLIIXVLYLW\EHWZHHQWUHDWPHQWVZDVH[DPLQHGIURP5DGRQHIIOX[ “ VWDQGDUGHUURU  DVVXPLQJKRPRJHQHLW\LQWKHGLVWULEXWLRQRILWVSDUHQWPDWHULDO 5D DFURVV H[SHULPHQWDOVLWHV6XUIDFHIOX[HVRI5QZHUHPHDVXUHGXVLQJWKH5$'5DGRQ 0RQLWRULQJ6\VWHP 'XUULGJH &R86$ LQUHFLUFXODWLRQPRGHDWWDFKHGWRDVWDWLF $FNQRZOHGJHPHQWV FKDPEHU)OX[HVZHUHFRUUHFWHGIRUSUHVVXUHKXPLGLW\DQGWHPSHUDWXUHIRUDFFXUDF\ 7KLVUHVHDUFKKDVEHHQIXQGHGE\'2(6XSSRUWIURPWKH7RROLN /DNH)LHOG6WDWLRQVWDIILVJUHDWO\DSSUHFLDWHGDVLVWKHORJLVWLFDOVXSSRUWRI&+0+LOO3RODU6HUYLFHV Sensitivity of Permafrost in the Arctic (SPARC) A multiscale perspective

Julia Boike, Moritz Langer, Sebastian Westermann, Sina Muster, Anna Abnizova, Konstanze Piel, Niko Bornemann, Britta Kattenstroht, Katrin Fröb

Our Helmholtz University Young Investigators Group examines heat, water and carbon fluxes in the Arctic permafrost system at different sites and their variation across multiple spatial and temporal scales. This poster summarizes recent key findings of the three PhD students‘ research topics: land cover, surface temperature and energy balance. Such comprehensive data sets are sparse for the Arctic and are of great value to support modeling efforts on the present-day and future arctic climate and permafrost conditions. SURFACE TEMPERATURE LAND COVER Satellite and ground based surface temperatures Especially water bodies play a large role in the water and measurements show that the spatial surface temperature energy budget of Siberian polygonal tundra: despite their small variations of wet polygonal tundra greatly reduce for surface area, small ponds and lakes have a large impact averaging periods longer than the diurnal cycle. The validity contributing about 30% to total evapotranspiration. In the Lena of surface temperature averages derived from satellites is Delta, 50% of the water bodies are smaller than 10m in therefore not affected by unresolved landscape diameter and not accounted for in existing land cover heterogeneities, except for free water bodies. High classifications. Correct land cover statistics within a large grid resolution land-water masks are therefore essential for the cell can be used to capture the subpixel heterogeneity. interpretation of satellite LST products. Land cover class dry tundra 100 dry tundra C A wetwet tundra tundra B overgrown water A B 6.2 13.5 openovergrown water water 80 8032025 8032025 open water 13 66 60 12.5 5.8

12 12 5.65.6 40 8032010 8032010 11.5 5.4 Land cover ratio [%] ratio cover Land 20 11 5.25.2 10.5 Land cover ratio [%] 0 8031995 8031995 p 5 10 10 0250500125 123456789 30.07.08 - 06.08.08 27.08.08 - 03.09.08 Meters 4.84.8 Landsat spectral class 415135 415150 415165 415135 415150 415165 Landsat spectral class Weekly averages of surface temperatures at polygonal tundra (A) land cover map (0.3m resolution), (B) spectral classification of site (~ 100 m2). (A) Sustained surface temperature differences Landsat (30m resolution), (C) each spectral class of the Landsat only occur if incoming solar radiation is high, (B) during periods image shows a distinct subpixel land cover composition. with intermediate and low incoming solar radiation differences average out.

SURFACE ENERGY BALANCE METHODS The surface energy budget determines the surface • satellite data (MODIS, Chris/Proba,Landsat temperature and thus the seasonal thawing of the soil. ETM+, TerraSAR-X, SPOT) During polar night conditions in winter, the long-wave radiation, the sensible heat flux and the heat input from the refreezing active layer have been identified as the main • mobile micrometeorology components of the surface energy budget. The incoming stations long-wave radiation is the determining factor for the surface regional scale • high resolution VNIR temperature of the snow, but a significant influence imagery particularly of the sensible heat flux remains. Summer Winter meso scale local scale ˂S L -122 ˂ Qh •permanent +28 ˂L ˂S -16 Q Q micrometeorology stations +43 h e -0.4 Q +22.5 +22.5 e +2.5 point scale •stationary TIR cameras C +22 C -9 Qg +12 Qg -5

Surface energy balance on Samoylov Island / Northern Siberia. Net short-wave radiation ˂S, net long-wave radiation ˂L, • temperature, moisture sensible heat flux Qh, latent heat flux Qe, ground heat flux Qg, residual C (energy balance closure term). Units in W m-2. • thaw depth, snow, soil physical properties FB Geo, Peri POF PACES 1.5

Julia Boike, Alfred Wegener Institute for Polar and Marine Research, Potsdam, Germany

Predicting the vulnerability of permafrost carbon (C) to climate change requires simulation of the permafrost’s annual dynamics coupled with the C cycle, as well as the soil water status which determines aerobic or anaerobic decomposition of organic matter. Quantitative long-term water and energy balance studies are particularly scarce circumpolar and almost absent from Siberia, but are of great importance for the validation of climate and permafrost surface schemes within climate models. Furthermore, due to the complex nature and non linearity of processes, quantitative predictions vary largely. The HGF group “Sensitivity of the permafrost system’s water and energy balance under changing climate: A multiscale perspective” (SPARC) investigates the carbon, water, and heat flux cycles in the complex Arctic landscapes at scales that range from metres to kilometres and thus successfully closes the gap between the small scale processes and larger scale remote sensing and modeling. In a truly original approach we combine field measurements of permafrost processes, pools, and fluxes, with remote sensing data and numerical climate models at local and regional scales. A quantitative process understanding is developed by identifying key processes of the seasonal and annual energy, water and carbon balance and the factors that affect these processes. This includes: representation of sub-grid cell variability in the landcover (especially with regard to water bodies), the quantification of the annual surface energy balance, the importance of snow cover formation and ablation for the permafrost thermal regime and the relationships between thermal and hydrologic processes and carbon cycle.

The SPARC results have the following implications: (i) current CO2 emissions are potentially underestimated from vast areas of the Arctic since small water bodies are commonly not included in the global land surface classifications, (ii) small variations in hydrologic budget (precipitation, evaporation, or drainage) have the potential to produce considerable changes in the CO2 emission and (iii) earth system modeling faces a formidable problem of scale (example Arctic ponds: important component, but too small to be represented in land surface schemes used for GCMs).

Poisson-Voronoi Diagrams and the Polygonal Tundra F. Cresto Aleina1,2, V. Brovkin2, S. Muster3, J. Boike3, L. Kutzbach4, T. Sachs5, and S. Zuyev6

-(
 " 
/(





&$!
%!0
0
'"!!8'!
(,&'
'
$#
,#"#(!
($


) 
  
* +(
) 
  
* +( 



 


%6
$
&! ')!!0
$&&(!0
$"%,(
!#8("$'%&
,/'
9664
%(!#'
#
#&(

$ ''$#
%$ #(
%&$8''
#
#&(

$ ''$#
%$ #(
%&$8''
#
$"%,(
&#$,'
'
,/'4
.
 ')#, '
>
%& ! !
#- &$#"#(':6
((#
''$ (
. (

%$ #(
%#
''$ (
. (

%$ #(
% 


 &#(
(&& #
(0%'6
5



"%(
$
!$!
(&$# )'

'
%(,&
$#!0
0
!$!
$&$#$
%$!0$#
9%$&$#$
%$!0$#
9% :6:6
"# ')
"$!'4
,(
(0
&
,#!
($
'& 
& $#!
$&


:
98(:
B3








#(&'





 !$!
('6



 &#(
$!$,&'
&%&'#(
 &#(
 &#(
$!$,&'
&%&'#(
 &#(
:
78(7C












 
#(&'



'()')!
'& %)$#
$
',
'0'("'
"0

!
($
,%'!
&(& ')'
$
(
(&& #
9 664
&(& ')'
$
(
(&& #
9 664
:
98(:
A8







#(&'



= 10 cm



! ")
&'%$#''
#
,/'
&$"
!$!
(,&'
($
!&
'!'6
,"  (0:,"  (0:6
6


'
'(,0
($
('(
(
%%&$5
%$!0$#!
(,#&6
0(
" 
.(
% 
"
%  
# 


" 
  


 




$!0$#!
(,#&
'

(0%
$


(&
(!
!-!
-& '
. (



#(&$##)$#'
"$#
%+&#
&$,#
#&(
0
%& % ()$#4
-%$(&#8 %$!0$#'
/%! #
'!$.
$"%!/
&
#
&$.(

'% &)$#
9! ")
$& #:
& #6
%&$'''6

#
!(&!
&,#$6



(
" #!0
$#' '('
$
!-(
&0




%%! )$#
$
%&$!)$#
& "'
#
!$.
.(
#(&'6


(&!
&,#$
( '
%!
5



"
.5
#'"!
' ",!)$#'6
#!
9:
 '%!0'
.(&
(!
(&'$!
($&0
$#
6
?
" '' $#'
%#
'(&$#!0
$#


(
A
.
%(

(
%$' )$#
$
(
.(&
(!
!-!
"
-5
& !
% (,&
$
(
-& )$#'
$-&
)"
!$#
. (
,",!)-
%& % ()$#
#


/%& "#(!
' ('
$#
"$0!$-
-%$(&#'% &)$#6






#
(
'
,#"#(!
#
(

9(:
#
&'%(
($
(
%$!0$#


(&
$.'
$,(
&$"
(
'!#4
#
-&
!(4
 & 
#!
9:
'$.'
.(&
(!
0#" '
#
(
(&
' ",!(





.(&
!#6


'0'("
(&$,

 #(
#(&
',&
9:6
9$ 
(
!64
=<<@:6

'#& $'5
.(
9!,
! #:4
&0
9&
! #:4
#
'(#&
9!
"
05
&$!)$#
&! 1)$#6

 #(
!,'(&
$
$##(
! #:6


!,'(&
$
#(&$##(
%$!0$#'
'
$!$,&

#
0!!$.6
%$!0$#'6
2(


 


&0
.(
',""&'
.$,!
!
($
' # #(


"$ )$#'
$
(
&)$#
$
(
!#'%


•
F. Cresto Aleina et al., (2012), A stochastic model for the polygonal tundra based on Poisson- $-&
0
'(,&(
#(&'6
1(

"
Voronoi Diagrams. Earth System Dynamics, in
revision. •
S. Muster et al., (2012), Subpixel heterogeneity of ice-wedge polygonal tundra: a multi-scale

  
 



% 
"

#
analysis of land cover and evapotranspiration in the Lena River Delta, Siberia, Tellus B.

 &#(
',&
(0%'
&
''$ (
($
•
J.Boike, et al., (2008), Climatology and summer
energy and water balance of polygonal tundra "
%

(

in the Lena River Delta, Siberia, Journal of Geophysical Research.  &#(
" '' $#
%&$%&)'6
"
/5
Our model shows increased
1 International Max Planck Research School for
Earth System Modelling, Hamburg, Germany methane emission in the wet scenario
2 The Land in the Earth System, Max Planck Institute for Meteorology, Hamburg, Germany because of a drastic drop in the area 





" 
 ) 
"$


3



'#& $
!'
($

&$%
#
(
',&
Alfred Wegener Institute for Polar and Marine Research, Research Unit Potsdam, Potsdam, covered by the relatively drier tundra
$ 



  
"

 
(
Germany $-&
0
& &
(,#&4
#
(&$&
($

4 Institute of Soil Science, Klima-Kampus, University
of Hamburg, Hamburg, Germany (moist centers and elevated rims). 5 Deutsches GeoForschungsZentrum, Helmholtz-Zentrum, Potsdam, Germany  &
"(#
" '' $#'6


6 Department of Mathematical Sciences, Chalmers University of Technology, Gothenburg, Sweden 
 

" "
 & 


 (


[email protected]

Research Statement: Fabio Cresto Aleina

Sub-grid and small scale processes occur in various ecosystems and landscapes (e.g., periglacial ecosystems, peatlands and vegetation patterns). Such local heterogeneities are often important or even fundamental to better understand general and large scale properties of the system, but they are either ignored or poorly parameterized in regional and global climate models. Because of their small scale, the underlying generating processes can be well explained and resolved only by local mechanistic models, which, on the other hand, fail to consider the regional or global influences of those features. A challenging problem is then how to deal with these interactions across different spatial scales, and how to improve our understanding of the role played by local soil heterogeneities in the climate system. This is of particular interest in the northern peatlands, because of the huge amount of carbon stored in these regions. Land-atmosphere greenhouse gas fluxes vary dramatically within these environments. Therefore, to correctly estimate the fluxes one needs a description of the small-scale soil variability. Applications of statistical physics methods could be useful tools to upscale local features of the landscape, relating them to large-scale properties. To test this approach I considered a case study: the polygonal tundra. Cryogenic polygons, consisting mainly of elevated dry rims and wet low centers, pattern the terrain of many subartic regions and are generated by complex crack-and-growth processes. Methane, carbon dioxide and water vapor fluxes vary largely within the environment, as an effect of the small-scale processes that characterize the landscape. It is then essential to consider the local heterogeneous behavior of the system components, such as the water table level inside the polygon wet centers, or the depth at which frozen soil thaws. I developed a stochastic model for this environment using Poisson-Voronoi diagrams. The model is able to upscale statistical large-scale properties of the system taking into account the main processes within the single polygons. I then compared the results with available recent field studies and demonstrated that the model captures the main statistical characteristics of the landscape and describes its dynamical behavior under climatic forcings (e.g., precipitation and evapotranspiration). In particular, I modeled and analyzed water table dynamics, which directly influences greenhouse gas emissions and changes in the system. Hydraulic interconnectivities and large-scale drainage are also investigated through percolation properties and thresholds in the Voronoi-Deleaunay graph. I plan to generalize this approach to other landscapes (i.e., northern peatlands and wetlands, as well as thermokarst lakes) in order to realistically estimate the large scale land-atmosphere physical and biogeochemical. To do that, I definitely need a deeper understanding in peatland dynamics, biology and carbon cycle. Further developments involve linking these results with the MPI Earth System Model, to explore the possible influences and feedbacks of the local features on the Earth's climate.


-.-+(- 1
=-,(+
T
,3' ,4+
,. +
.1
/'2
31-2(4.-2
(-
, 3'-
&2
'=13 2
;(3'(-
/ 1, 1.23
9- 1
+(,3
'-& C

N. S. Duxbury S

@

-2N+9,-(C+3 'C 9

-9<91=N&,9C 9


V. E. Romanovsky 2, N. N. Romanovskii 3, L. S. Garagulya 3, A. V. Brouchkov 3, I. A. Komarov 3, L. T. Roman 3, G. S. Tipenko 4, S. N. Buldovich 3, L. N. Maximova 3 Affiliations: 1: George Mason University; 2: University of Alaska Fairbanks; 3: Moscow State University 4: Inst. of Environmental Geoscience, RAS.

 

  
& 

 

 

"

 
#  
$ 




 


2-31

0//


1//
 *

I
 F+(*
.,/.9-2@
93
 VLX T
23+
3

a
SZ
@
 







 / -(-&
9/.-
/1 2291 C
9
3.
3'

1 + 2

.
+3 -3

' 3
.
+3'13
.1,4.-
•  - 314.-
.
3'
291 
3 ,/ 1391
;: 2

I 
&2
&9 23

,.+ 9+
(2
31// 
(-

&
.
3'
9(
;3 1F(
+7
J .1

2319391
K@
C&C@
TL
X TA




.
 VLX T`WY
EJ,.+
.
TK
b

 VLX TC




JF;: 2K
(2
3'
92
.
/'2
31-2(4.-2
(-
















.
T(






`

X

E,.+@
I '
/'2
31-2(4.-


 / -2
9/.-
/1 2291
J' -
.-
 /3'K@
C&C@
.1
 VLX T
B
 VLX T
 /.2(32C





3'
(-3 1-+
' 3
$.;
.:
3'
+3'13
 /.2(3
(2



H/'2
`
L+-

_
@

;' 1
F/1 2291 A
@

F
.-23-32
J 1.,


+3 -3
.
 VLX T`WY
EJ,.+
.
TK
b

.
T
( `

X
E,.+
A nonlinear dynamical 2D numerical model for I (,(+1(4 2
;(3'
T
(
,*
-=
1 ,.3
 3 4.-
(#9+3C
the effect of latent heat of phase transitions in Period-average surface temperature -10 0 C and -11 0 C
gas hydrate on the shape of the overlying Temperature amplitude 8 0 C ; 10 0 C; 13 0 C
permafrost front Geothermal grad T = 0.023 K/m
 3'-
+3'13
'=13
29//.14-&
(32
.;-

.,924.-C
(system of PDEs): Base of CH4*6H2O deposit is at 700 m, 820 m.
Semi-minor axis of of CH *6H O deposit 100 m and 260 m,
 3'-
=13
F
∂T 4 2 ρ(T, x,y)c(T, x, y) = div(λ(T, x,y)∇T), where T(x,y,t) ≠ Tphase(x,y). (1) where the deposit shape is an upper half ellipse. Same 0 rad '
2
 2.91
.
3'
∂t phase.
9391 C
T(x, y,t) = Tphase(x, y): 0 deg C for permafrost and Aln P + B, where A, B –constants;

∂Φi " 

"C
Qi = (λ∇T | G + 0 − λ∇T | G − 0,∇Φi), , i=1 for permafrost, i=2 for clathrate deposit; ∂t

T(x,0,t) = −4.4 − 8*SIN(2πt /P) - the upper boundary condition for West Siberia. 
 &

#&





• 91
.,/934.-2
2'.; 
3'3

3'
(-$9 -
.
+(,3
Where P = 40 Ka. For other cases P = 100 Ka, P = 200 Ka and 
  
1 2 3 .2(++4.-2
;(3'

+.-& 1
/ 1(.2
':
,9'
+ 22
! 3
.-
'-& 2
superposition of these harmonics, c – heat capacity,
(-
/ 1, 1.23
2
+.4.-
J 2/ (++=
.1

3'
/ 1(.
.
TRR
KC
Tphase = A ln P +B for CH4*6H2O.
• '
.1,4.-E(22.(4.-
.

, 3'-
+3'13
.91
1.,
3'

• SZSRB
(1
:=A
'+.1(-
'=13










9// 1
291 

.
3'
 /.2(3C
•




+TLSR TA
SZTUB
1=
.-"1, 
"  

    B
• '
291 
F;: 2
1
09 -' 
=
3'
/'2
31-2(4.-2
(-
3' S

• 
91(.2(3=
9-4+
(2.: 1 
(-
S[UV
3.
+.*
&2
/(/ +(- 2
3

b
( H, +4-&
+3'13
 /.2(3C
•




J2(-
&2
(2
3
'(&'
M
WR
3,KC
•




-
.1,

/1.) 4+
-
1 '

/(/ C


• .1
3'
`VR


3
 13(-
/.'2
3' 1
1
T
/'2
1.-32
(-
• -
S[XY

, 3'-
+3'13
'=13
;2
(2.: 1 
9- 1

( 1(-
/ 1, 1.23
• We
have developed permafrost - carbon physical and numerical
3'
 /.2(3
,.:(-&
(-
3'
.//.2(3
(1 4.-C
•




J91((

*.&.-
3
+C@
S[XYK

models, where carbon is in the form of methane

clathrate hydrate ( CH *6H O ) in a porous subsurface environment.
•
• S[YVB

 VLX T
(2.: 1 
(-
2 (, -32
9- 1
MT
*,
.
3'
+*
 
4 2
9, 1(+
-+=2(2
.
3 ,/ 1391
'-& 2
;(3'
 /3'
2'.; 
• .

 3 4.-
= 3
.
<313 11 231(+
+3'13

'=13 2@

3'.9&'
TLX T
292/ 3 
.-

J3
(! 1 -3
/.'2K
3'3
3'
&1 3 23

:+9 2
.

&1

J-
3'92
12@
., 32@

3'
..-
J C&C@
(++ 1
-
,=3'
S[YRA
,=3'
S[Y[A


.1.:.+2*(2@
• The driving force for the subsurface temperature field dynamics is
.
' 3
$.;2K

.91
(-
3'
 /.2(3D2
 -3 1

9- 1
3'
/ 1, 1.23
-& 12.++@

S[YW



9<91=
3
+C@

@
TRRSKC

climate variations on the Earth’s surface. This is an upper boundary
2 C
' (1
/.2(4:
:+9 2
1
/1 2 1: B
3'
(-3 1-+
' 3
$.;
(2
condition for the nonlinear evolutionary system of PDEs.
- : 1
1 : 12 
9
3.
3'
/'2
'-& 2
(-
3'
+3'13
 /.2(3C



• The developed numerical model is a valuable computational tool to
•
91
-9, 1(+

• After achieving the periodic regime (after 4-5 periods), the temperature distribution can be used as an initial subsurface  11 231(+
2
:2C
14-C
Methane Hydrates - the World’s Largest temperature for the contemporary upper boundary temperature Untapped Fossil Energy Resource
changes (on decadal timescale).
• A combination of lower average annual surface temperatures on Mars and a smaller geothermal gradient would increase the vertical extent of the 
':
/ 1 .1, 
.,/93 1
2(,9+4.-2
92(-&
3'
3' 1,./'=2(+
clathrate stability zone by several times. '13 1(242
J' 3
/(3=@
 -2(3=E/.1.2(3=@

3' 1,+
• The change in the annual average insolation caused .-94:(3=K
.1

3'

( 1(-
29291  C

by variations in Mars
eccentricity is much smaller than 3
-

92 
2

3 11 231(+
-+.&
.1
3'
14-
29291 
that caused by variations in obliquity and the

J C&C@

9<91=
3
+C@
TRRSKC

precession of L_s (season) of perihelion (period ~50 Ka; Mellon and Jakosky, 1992 91
,. +

3* 2
(-3.
.9-3
3'
 / - -
.

29291 
KC 3' 1,./'=2(+
'13 1(242
.-
3 ,/ 1391

-

2/4+

• The obliquity oscillations have greater amplitude on Mars than on Earth Jakosky et al., 1995). Also, Mars
..1(-3 2C



+

obliquity oscillates with a longer period of 120
Ka (Ward, 1974), hence, the corresponding surface T- wave penetrates deeper (than the surface T-wave with  "
  
#"
 
the period of 40 Ka for the Earth
s obliquity cycle). • Hence@
the effect of permafrost base deviation
is greater on Mars compared to Earth.




'
2(&-("-3
! 3
.

0.05 +3 -3
' 3
(-
 VLX T
.-
&1

-
3'92
.-
3'
2-31
#"
1-31
.: 1+=(-&
/ 1, 1.23

1.-3C


0.04 • -
13'


M[[
^
(2

 LX A


LX 
(2
11 @
3'.9&'

/14+
.





'
+9
-
3'
9// 1
V T T T 
(-
3'
3,.2/' 1
(2


MSRR
4, 2
3'3
.

J;'('
(2
.-+=

31
+*
+(-
3
SZ

J /.'
T V &2KC
0.03 .
 LX 
.1,4.-KA
V T • 

Sloan 1990
.-(4.-
(2
RCRTU
E,

>.-
.1
TLX T
(2

2(,(+1@

93
2'(% 
+.2 1
3.

3'
14-






291 
JMW,
 /3'@
<
-
+(!.1@
TRRRKC



 

#*







 
"
)


•'
! 3
.

+3 -3
' 3
(-

• 9<91=
C
C@
C
=>.:@
3
+C
TRRXC
(,
,'(- B
-( -3
(
.-
 VLX T
.-
3'

' 3
$.;
13'
-
(-
3'
.2,.2@


@
J 391



14+ K@
, 1(-
.:
-

3'92
.-
3'
 ./'=2(+
-(.-@
.+C

ZY@


OU[@

 /3C
TX@

VRS
G
VSX
C

.: 1+=(-&
/ 1, 1.23

1.-3C
• C
C
=>.:@
C
C
9<91=(
3
+C
TRRXC
9/ 1F+.-&
-(.2(2
.
-( -3

,(1..1&-(2,2
(-
(
-
3 11 231(+
,. +2
.1
 : +./, -3
.
• 1, 1.23
3'(*- 22
.:
, 3'.2
3.
2 1'
.1
+(
.-
12@
91./
-
.3' 1
/+- 31=
 23 1-

( 1(-

1.22F2 4.-
3'

 -3 1
.
3'
 /.2(3
J<`R
.( 2@
:- 2
(-
/
 2 1'(
.+C
UZ@
OX@
SS[SF
SS[YC

,K

,(-92
3'
,( -3
• 9<91=@
C
C@
-
C
.,-.:2*=@
TRRVC
 1,- -3
2 09 2314.-
.
/ 1, 1.23
3'(*- 22
J<`ZRR
,@
,(6 
&2 2
(-
3'
.1,
.
+3'13
'=13 2@

 'C
1( 2

;' 1
3'
(-$9 -
.
.91-+@

URTWX@
.+C
TZ@
229
T@
WWC

+3'13
/'2
31-2(4.-2
-
• 9<91=@
C
C@
-
C
.,-.:2*=@
TRRUC
 3'-
+3'13
'=13

- &+ 3 KC


bR

91(-&
/1.2/ 4-&@

 'C
1( 2
.91-+@
FURTWY@
.+C
TY@
O
ST@
VYC

S
 LX 


(22.(4.-C
0

R
 &

0
V T • 9<91=@
C
C@
C
C
.4*.:@
C
C
 +2.-@
C
C
.,-.:2*=@
C
bR
0H1 9 
&1.9-
;3 1
12 =@

TRRSC

-9, 1(+
,. +
.1
-
+3 1-4:
.1(&(-
.
*
.23.*
-
(32
<.(.+.&(+
(,/+(4.-2
.1
12@
C
 ./'=2C
 2C
(
.+C
SRX
@
.C
S@
SVWU
F
SVXTC

UTR
,

2.1/4.-
.
+3 -3
' 3
.-
(22.(4.-
1.-3
+3'13
&2








VRR
,
WRR,
0

(-3 1-+
' 3
$.;
Natalia Duxbury, George Mason University, USA ([email protected])

Computer modeling of nonlinear dynamical thermal processes (heat and mass transport) with multiple phase transitions (multiple moving interfaces) in 2D cases. Specifically, numerical modeling of nonlinear Stefan-like problems in heterogeneous subsurfaces: changing climate - permafrost - active layer - methane clathrate hydrate systems. Computer modeling for hazards of thawing subsurface permafrost and melting of ice in Antarctica (with subsequent methane emissions from gas hydrates) due to climate change.



# "#&$

$
## $%
 
$ ,
+
 $

$$
#  *4/5/
# $
#! 6/
3
(+
 $% 4/5
4/5 4 %)#
$ )#
  ,
 #% #,/
  #
%%
 *#$%,/

5#)%
#
# #

  ,/
  #
%%
 *#$%,/

61
 %#
#%
 $
$#
%& /


1. Background J=
-2%.-
• Permafrost soils contain large carbon (C) stocks1
 
-*.,%
3, -
6
*.! • Warming may increase decomposition rates, resulting in

CH4 & CO2 release to the atmosphere • K • Decomposition of soil C is dependent on interactions ILHF
&?G<
%2&"'(?-"%".-
'
%6- =
between physical, chemical and biological components • (,
*,(&"''.
"'
*,&,(-.
'

&"',%
of soils2 • L • The chemistry of the soil C plays a central in IJFF<

'

('- :
--(".
4".!
,((.
,(4.!
'
M determining its decomposability *(--"%6
,'.
"/('
(
%" !.
,/('
*%'.
&.,"% =

• 5"-.-
"'
%%
-&*%-;
&(,
*,(&"''.
"'

(,
• Contrasting results between bulk soil C:N2,3 and incubations3, 4, DOC/TDN yield, and NMR4 studies • suggests that bulk soil metrics, such as C:N, are not HNMF?HOKF<
%"*!/

('-=

!"-
'
"-
--(".
4".!
sufficient indicators of permafrost organic carbon !" !
-("%
(, '"
&1,:
'
"-
%-(
$'(4'
.(
,-
chemistry 2,"'
(&*(-"/('=
• 5"-.-
"'
%%
-&*%-;
&(,
*,(&"''.
"'

(,
• Thus, we need to better understand the chemical complexity of the permafrost organic matter (OM) in • L order to improve predictions of carbon release from GOMF?GNMF
C
GFNF<
+2,.7
('.'. =

thawing permafrost. • %%
&"',%
-("%-
)#
40
3, 
 
-*.,
(
.!
/3
%6,
'
.!
*,&,(-.
-&*%-=

• GMJF<
,(&/
Q
"'
,-"-.'.
-("%

,/('-
'
%" '"'=
H=
5*,"&'.%
#/3

• .%( 2
.!
!&"%
2'/('%
,(2*-
.!.
,"'"*%
(&*(''.-
'%6-"-
AB
(
.!
2'-!
-("%
-*.,
(&*,"-
.!

"'
*,&,(-.
2-"'
(2,",
• 
(, '"
-&*%-
-*,.
,(&
*,&,(-.
A'

&"'B
('
(&*(''.
G
,'-(,&
"?"',,
A B
-*.,(-(*6
'
AB
• , '"
 ?
!" !
(&*(''.
G
AGB

-(,-
(&*,
.!&
.(
.!
/3
%6,=

• "'
 ?
%(4
G:
2.
!" !,
I
,%/3
.(
.!
*,&,(-.

• G
%("' -
"'".
.!.
.!
-&*%-
,(&
.!
*,&,(-.
-(,
.
ILFF
'

I=
.!(-

(, '"
GNMF
&?G
A-'-
'
%6-B

• /3
%6,
A(, '"
'

&"',%

,&
F?GK&
&"',%B
C
*,&,(-.
-("%-
,&
GLP
&
• , '"
 ?
IJFF
A > B:
HOIF
A@ B:
GMJF
A,(56%"
"-
'
&"-B:
GHHK

4,
(%%.
,(&
 4('
A,(&/
@ B:
'
GFOI
A*(%6-!,"-B=


"%%-:

"'
N>HFFO
2',
&("-.
• ,

!-
&(,
-"%6
(&*(-%
?
IJFF:
HOIF:
GHHI
,-
""
.2',
3 ./('
2*('
"'2/('M=

• ,&,(-.
-("%-
4,
AB
-*,.
"'.(
K&
"',&'.-
• ,

"-
!,.,"7
6
!" !,

('.'.
'
&(,
,'.
"/('
(
(, '"
&.,"%-=
• ("%-
4,
-''
"'
"? 
AJFFF
.(
JFF
&?GB
('

" "%

MFFF
(2,",
.,'-(,&

(
*,&,(-.
%('
A 2,
'(.
-!(4'B
-*.,(&.,
A,"':
'=:
%(
%.(:
B



• *.,
4,
(%%.
.
J
&?G
,-(%2/(':
4".!
• !
*,&,(-.
-*.,
",
(,"'
.(
*.!
LJ
(?
-'-
*,
-*.,2&
•
,&,(-.
F?GK&?
!" !,

G
-(,-
,%/3
.(
*,&
GLP
&

• )#
5=
AB


(
 
,&
F?GK&?
-(,
&(,
.
.!
(, '"
'-
.
IJFF:
• 2*%".
-'-
(
!
-&*%
4,
*,(,&
'
"'%2
.
,(&
-("%-
(%%.
HOIF:
GMIF:
GKKF:
'
GHHK=
!-
'-
'(&*--
"'
.!
*,"'"*%
(&*(''.-
'%6-"-
AB=
%%
-*.,%
.
",'.
*.!-:
'
",'.
(,&-
(

,(&
%"%
IJFF:
.(
,-"-.'.
GHIF=
3, -
4,
%2%.
2-"'
 >
-( 4,:
,-"('
AB
(&*(''.
G
%("' -
• ,&
GLP
&?
-(,
&(,
.
.!
&"',%
'-
(,
%6
O
A!,&(
%/:
%&:
 B=
(,
.!

-!(4'
"'
AB=
'
+2,.7=

L=
"-2--"('
• 2%$
!&"%
'%6-"-
(
*,&,(-.

!-
"'".
.!.
".
"-
&(,
A
C

6"%;
 BJ:
+2%
.(
A<BJ
(,
%--
A<BI
%"%
.!'

-("%-=
'2/('
-.2"-
,*(,.
.!.
*,&,(-.

!-
+2%I
(,
,.,I=J
%"%".6
.(
.!
(, '"
/3
%6,=
"'".
*,&,(-.

"-
&(,
%"%J=

• 2,
 
,-2%.-
-!(4
.!.
.!,
"-
(.!
'('?(&*(-
'
*,(--
&.,"%
*,-'.
"'
*,&,(-.
-("%-=
• ,!*-
.!
!" !
,-*",/('
,.-
,(&
*,&,(-.I:J
'

5*%"'
6

%$
(
&"',%
--("/('-
.
.!
/&
(
.!4=

• 2.2,
"'2/('
-.2"-
'

'%6-"-
,
*%''
.(
"'3-/ .
.!"-
2,.!,=


J=
%,(*:
=


 
 
 
2HFGFB=

M= ,'-
K=
 26':
=






AGOOGB=

G=
,'(":
=


 
 
 
AHFFOB=
L=
,!2,:
=
'
,7$:
=
 
 
2GOOOB=
M=
%,)':



=


 


=
HFGGB=

H=
!&".:
=


 
AHFGGB=

I=
:
=


 
 
 
AHFGHB=

N=
$'(4% &'.-<
!"-
,-,!
"-
2'
6
.!
/('%
"'
(2'/('

(
(%,
,( ,&-:
.!

,2.
-,!
%%(4-!"*
,( ,&:
'
.!
*,.&'.
(
', 6
%(%
!' 
2/('
,( ,&=
,3%
-2**(,.
!-
'
',(2-%6
4,
.(
&
6
.!
,/
=

Jessica Ernakovich Ph.D. Candidate, Graduate Degree Program in Ecology, Colorado State University

In my dissertation research, I am investigating the sensitivity of permafrost carbon and microbial activity to changes in temperature, specifically thaw.

My first three chapters are mainly observational studies of the permafrost soil organic carbon pool and the genetic and functional diversity of the microorganisms frozen in permafrost:

1. “The Chemical Properties of Alaskan Permafrost and Seasonally Thawed Soils” (in prep) a. Analysis of permafrost and active layer carbon using Fourier Transformed Mid-infrared Spectroscopy (FTIR) 2. “Temperature sensitivity of growth and functional diversity of microbial communities” (in prep) a. Investigation of growth kinetics and substrate use of permafrost and active layer microorganisms on different carbon and nitrogen substrates. 3. The genetic diversity of permafrost and active layer soils a. Microbial diversity assessed using 454 pyrosequencing

In my current research, I am doing a manipulative experiment to investigate the temperature sensitivity of CH4, N20 and CO2 emissions from permafrost soils under oxic and anoxic conditions. In this laboratory incubation, I will attribute gas flux to changes in carbon chemistry of the dissolved and total soil carbon pools (using FTIR (see 1a) and fluorescence excitation- emission spectroscopy (EEMS)), changes in microbial parameters (using enzymes and microbial biomass), and physical soil characteristics (by controlling redox conditions, and measuring soil Fe, P, and N).

My upcoming research project will investigate whether microbial community composition in permafrost is controlled by substrate availability (as observed in other soils), or whether temperature plays a larger role in structuring the permafrost microbial community. Modeling the effects of fire severity on soil organic horizons and forest composition in Interior Alaska

Genet H.1, Barrett K.2, Johnstone, J.3, McGuire A.D.1,4, Yuan F.M.5, Euskirchen E.1, Kasischke E.S.6, Rupp S.7, Turetsky M.1,8

1 Institute of Arctic Biology, University of Alaska, Fairbanks, AK, USA; 2 U.S. Geological Surve, Alaska Science Center, Anchorage, AK, USA; 3 Department of Biology, University of Saskatchewan,SK, Canada; 4 U.S. Geological Survey, Alaska Cooperative Fish and Wildlife Research Unit, University of Alaska, Fairbanks, AK, USA; 5 Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN, USA; 6 Department of Geographical Sciences, University of Maryland, College Park, MD; 7 Scenarios Network for Alaska and Arctic Planning, University of Alaska, Fairbanks, AK, USA; 8 Department of Integrative Biology, University of Guelph, Guelph, ON Canada.

The fire regime in the boreal region of interior Alaska has been intensifying in terms of both area burned and severity over the last three decades. The impact of fire on boreal ecosystems will largely be driven by changes induced on the organic layer. We used a process-based modeling approach to examine the response of the organic layer depth to fire and how this reponse influence carbon cycling, permafrost stability and vegetation composition.

Courtesy: AK Fire Service Courtesy: IARCC Introduction Results and Discussion

—Boreal forests represent the largest reservoir of soil carbon (C) among terrestrial biomes. Rising soil temperature is Prediction model of fire severity expected to cause permafrost degradation and enhance decomposition of the organic matter, potentially releasing large quantities of carbon in the atmosphere. Furthermore rising temperature and changes in precipitation regime is 1. The effect of fire on the loss of the organic layer 3. In flat areas, fire severity on the organic layer is also expected to increase the vulnerability of the organic layer to fire. thickness decreases in flat areas where water increased when fires occur late in the season, when —The organic layer is of fundamental importance in boreal ecosystems for its influence in soil thermal and hydrologic accumulation may prevent deep burning (figure 4). the soil is drier (table2). regimes that determine (1) active layer dynamic and permafrost stability, (2) water and nutrient availability for the vegetation and (3) environmental conditions for regeneration (figure 1). 1. Regardless of the landform, large fires induce deep 4. On slopes, elevated maximum vapor pressure deficit

—The response of the organic layer to fire depends on climate, drainage, aspect as well as fire characteristics. Given the burning. Furthermore, large evapo-transpiration of of the year preceeding the fire increases the loss of complexity of the interactions among the atmosphere, vegetation and soil, predicting the fate of boreal ecosystem in the summer preceding fire year is a predisposing organic layer as well (table 3). response to a changing climate and its associated consequences on fire regime remains a challenge. factor for deep burning the following year.

In the present study, we used a process-based Table 2: Flats (slope < 1.5o) – R2 = 0.297 Table 3: Slopes (> 1.5o) – R2 = 0.402 modeling approach to examine the response of Slope <= 1.5o Slope > 1.5o Parameter estimate Parameter estimate for the organic layer depth to fire and how this Figure 4: Observed organic layer loss after fire in Variable Importance Variable for centered scaled Importance in sites with a slope lesser or greater to 1.5o. centered scaled data response influences permafrost stability, carbon data

cycling and vegetation composition. Figure 1: Soil organic layer dynamics across fire and Date of burn 0.243 1.222 slope 0.071 1.001 successional cycles representing alternative stability domains Elevation 0.131 0.760 Area burned 0.0766 1.081 (Johnstone et al. 2010). Area burned 0.193 0.974 Max. annual VPD (n-1) 0.232 0.861 Max. annual ET (n-1) 0.210 0.059 Modeling Approach Max. annual ET (n-1) 0.152 0.936

Existing field observations were analyzed to build a predictive model of the depth of burning of soil organic horizon 5. After integration of the model, TEM after a fire. This model was then integrated to the Terrestrial Ecosystem Model to quantify the post-fire soil organic successfully reproduce the post-fire organic layer loss on C cycling, permafrost stability and vegetation composition. layer loss in the sampled sites (figure 5).

Predictive model of fire severity on organic layer depth: data analysis

Observations: Figure 5: Model integration– site specific comparison of 178 sites dominated by black spruce stands in Interior Alaska (figure 2); observed and simulated loss of organic layer thickness Next step: Assessing the effect of fire on boreal ecosystems data collected in 31 fire events that occurred between 1983 and 2005. after fire.

Analysis: TEM with the newly implemented fire severity module will be run for the Alaskan Yukon River Basin using monthly Response variable: absolute loss of organic layer thickness climate data, soil texture and physiographic information at 1 km*1km resolution to: Analysis: Partial Least Square Analysis 1- Assess the impact of historical climate variability/change and fire regime on boreal ecosystems (figure 6); 2- Simulate the fate of C pools and fluxes and permafrost stability in the expected future climate (figure 7); and Table 1: List of the parameters tested in the fire severity model FIRE REGIME 3- Implement the dynamic vegetation module of TEM to reproduce post-fire vegetation succession.

INTENSIFICATION

Category Parameters 5 Climate Monthly radiation, precipitation, air and soil temperature,

vapor pressure deficit. YES NO 2.5 CRU cccma Ecophysiology Potential and actual evapotranspiration, transpiration, echam5 evaporation Figure 2: Map of fire perimeters sampled, coded by decade 0 (Turetsky et al. 2011). Fire characteristics Date of the fire, area burned YES x x -2.5

Topography Slope, aspect, flow accumulation, land form (upland, lowland, MAT (oC) -5 slope)

WARMING WARMING NO x x -7.5

Assessing the effect of fire on boreal ecosystems -10 Figure 6: Experimental design to assess the relative 1900 1920 1940 1960 1980 2000 2020 2040 2060 2080 2100 importance of rising temperature and fire regime YEAR The models (figure 3): intensification on boreal ecosystems for the historical The Terrestrial Ecosystem Model (TEM) is a process- period [1950-2011] Figure 7: Mean annual temperature (MAT, oC) over the AKYRB region from 1900 – 2006 based ecosystem model designed to simulate the historical period and 2007 – 2099 projected period. Historical temperature is based on downscaled data from the Climate Research Unit (CRU), and the projections are based on carbon and nitrogen pools of vegetation and soil, the downscaled GCM data from simulations by the CCCMA-CGCM3.1 (CCCMA) and MPI and carbon and nitrogen fluxes among vegetation, ECHAM5 (ECHAM5) models for the A1B emissions scenario. soil, and atmosphere (Raich et al. 1991).

Information about the fire regime is provided to TEM by The Alaska Framed Based Ecosystem Code Acknowledgement

(ALFRESCO), a model designed to simulate the This research is supported by the Department of Defense Strategic Environmental Research and Development Program response of subarctic vegetation to a changing through the funded project “Identifying indicators of state change and forecasting furutre vulnerability in Alaska boreal climate and disturbance regime (Rupp et al. 2000). forest” (research grant RC 2109).

References: Figure 3: Interactions among modules of TEM and ALFRESCO. TEM modules - Johnstone J.F., Chapin III F.S., Hollingworth T.N., Mack M. C., Romanovsky V., Turetsky M. 2010. Fire, climate change and forest resilience in interior Alaska. Canadian Journal of Forest Research 40: 1302-1312. include the daily environmental module, the monthly ecological module, the - Raich J.W., Rastetter E.B.,Melillo J.M., Kicklighter D.W., Steudler P.A., Peterson B.J., Grace A.L., Moore III B., Vörösmarty C.J. 1991 Potential net primary productivity in south america: application of a global model. Ecological Applications 1(4): 399-429. - Rupp, T. S., A. M. Starfield, and F. S. Chapin III. 2000. A frame-based spatially explicit model of subarctic vegetation response to climatic change: comparison with a point model. Landscape Ecology 15:383-400. annual fire effects module, and the dynamic organic soil module. - Turetsky M.R., Kane E.S., Harden J.W., Ottmar R.D., Manies K.L., Hoy E.W., Kasischke E.S. 2011 Resent acceleration of biomass burning and carbo losses in Alaska forests and petlands. Nature Geosciences 4: 27-31.

Modeling the effects of fire severity on soil organic horizons and forest composition in Interior Alaska.

Genet H., Barrett K., Johnstone J., McGuire A.D., Yuan F.M., Euskirchen E., Kasischke E.S., Rupp S., Turetsky M.

The fire regime in the boreal region of interior Alaska has been intensifying in terms of both area burned and severity over the last three decades. Based on projections of climate change, this trend is expected to continue throughout the 21st century. Fire causes abrupt changes in energy, nutrient and water balances influencing habitat and vegetation composition. An important factor influencing these changes is the reduction of the soil organic horizon because of differential regeneration capabilities of conifer and evergreen shrubs vs. deciduous trees and herbaceous vegetation on organic vs. mineral soils. The goal of this study is to develop a prognostic model to simulate the effects of fire severity on soil organic horizons and to evaluate its long-term consequences on forest composition in interior Alaska. Existing field observations were analyzed to build a predictive model of the depth of burning of soil organic horizon after a fire. The model is driven by data sets of fire occurrence, climate, and topography. Post-fire vegetation succession was simulated as a function of post-fire organic horizon depth. The fire severity and post-fire vegetation succession models were then implemented within a biogeochemistry model, the process-based Terrestrial Ecosystem Model. Simulations for 21st century climate scenarios at a 1 by 1km resolution for the Alaska Yukon River Basin were conducted to evaluate the effects of considering vs. ignoring post-fire vegetation succession on carbon dynamics. The results of these simulations indicate that it is important for ecosystem models to represent the influence of fire severity on post-fire vegetation succession in order to fully understand the consequences of changes in climate and disturbance regimes on boreal ecosystems. WĞZD͗WĞƌŵĂĨƌŽƐƚZĞŐŝŽŶĂůŝnjĂƚŝŽŶDĂƉĨŽƌ^ƚƵĚLJŝŶŐsƵůŶĞƌĂďŝůŝƚLJŽĨ WĞƌŵĂĨƌŽƐƚĂƌďŽŶ ^ĂŶƚŽŶƵ'ŽƐǁĂŵŝϭ;ŐŽƐǁĂŵŝƐΛŽƌŶů͘ŐŽǀͿ͕ĂŶŝĞů:͘,ĂLJĞƐϭ;ŚĂLJĞƐĚũΛŽƌŶů͘ŐŽǀͿ͕WĞƚĞƌ<ƵŚƌLJϮ͕'ƵƐƚĂĨ,ƵŐĞůŝƵƐϮ͕ĂǀŝĚDĐ'ƵŝƌĞϯ͕ĚǁĂƌĚ^ĐŚƵƵƌϰ ϭKĂŬZŝĚŐĞEĂƚŝŽŶĂů>ĂďŽƌĂƚŽƌLJ͕Ϯ^ƚŽĐŬŚŽůŵhŶŝǀĞƌƐŝƚLJ͕ϯhŶŝǀĞƌƐŝƚLJŽĨůĂƐŬĂĂƚ&ĂŝƌďĂŶŬƐ͕ϰhŶŝǀĞƌƐŝƚLJŽĨ&ůŽƌŝĚĂ

ďƐƚƌĂĐƚ͗ /ŶĐƌĞĂƐĞĚ ƚŚĂǁŝŶŐ ŽĨ ƚŚĞ ƉĞƌŵĂĨƌŽƐƚ ĐĂƌďŽŶ ƉŽŽů ŝŶ ƉĞƌŝŐůĂĐŝĂů ƚĞƌƌĂŝŶ ĚƵĞ ƚŽ ǁĂƌŵŝŶŐ͕ ĂŶĚ ƚŚĞ ƌĞƐƵůƚŝŶŐ ŵŝĐƌŽďŝĂů ĚĞĐŽŵƉŽƐŝƚŝŽŶ ŽĨ ƚŚŝƐ ĨƌŽnjĞŶ ŽƌŐĂŶŝĐ ĐĂƌďŽŶ͕ ŝƐ ĞdžƉĞĐƚĞĚ ƚŽ ďĞ Ă ƐŝŐŶŝĨŝĐĂŶƚ ƉŽƐŝƚŝǀĞ ĨĞĞĚďĂĐŬ ŽŶ ĨƵƚƵƌĞ ŐƌĞĞŶŚŽƵƐĞ ŐĂƐ ĨŽƌĐŝŶŐ ĨƌŽŵ ƚĞƌƌĞƐƚƌŝĂů ĞĐŽƐLJƐƚĞŵƐ ƚŽ ƚŚĞ ĂƌƚŚ͛Ɛ ĂƚŵŽƐƉŚĞƌĞ͘ dŽ ŶĞĞĚ ŝŵƉƌŽǀĞ ŽƵƌ ƵŶĚĞƌƐƚĂŶĚŝŶŐ ŽĨ ƉĞƌŵĂĨƌŽƐƚ ĐĂƌďŽŶ ǀƵůŶĞƌĂďŝůŝƚLJ ĂŶĚ ĨĞĞĚďĂĐŬƐ ŝƐ ŝŶĐƌĞĂƐŝŶŐůLJ ďĞĐŽŵŝŶŐ Ă ƌĞƐĞĂƌĐŚ ƉƌŝŽƌŝƚLJ ĨŽƌ ƚŚĞ ĂƌƚŚ ^LJƐƚĞŵ DŽĚĞůŝŶŐ ĐŽŵŵƵŶŝƚLJ͘ ^ƉŽŶƐŽƌĞĚ ďLJ ƚŚĞ EĂƚŝŽŶĂů ^ĐŝĞŶĐĞ &ŽƵŶĚĂƚŝŽŶ͕ ƚŚĞ ͞sƵůĞŶĞƌĂďŝůŝƚLJ ŽĨ WĞƌŵĂĨƌŽƐƚ ĂƌďŽŶ͟ ZĞƐĞĂƌĐŚ ŽŽƌĚŝŶĂƚŝŽŶ EĞƚǁŽƌŬ ;ZEͿ ŝƐ Ă ĐŽůůĂďŽƌĂƚŝŽŶ ĂŵŽŶŐ ƐĐŝĞŶƚŝƐƚƐ ǁŽƌŬŝŶŐ ƚŽ ƐLJŶƚŚĞƐŝnjĞ ĂŶĚ ůŝŶŬ ĞdžŝƐƚŝŶŐ ƌĞƐĞĂƌĐŚ ĂďŽƵƚ ƉĞƌŵĂĨƌŽƐƚ ĐĂƌďŽŶ ĂŶĚ ĐůŝŵĂƚĞ ŝŶ Ă ĨƌĂŵĞǁŽƌŬ ƚŚĂƚ ǁŝůů ŝŶĨŽƌŵ ďŝŽƐƉŚĞƌŝĐ ĂŶĚ ĐůŝŵĂƚĞ ŵŽĚĞů ĚĞǀĞůŽƉŵĞŶƚ͕ ĂŶĚ ĐŽŶƚƌŝďƵƚĞ ƚŽ ĨƵƚƵƌĞ ĂƐƐĞƐƐŵĞŶƚƐ ŽĨ ƚŚĞ /ŶƚĞƌŐŽǀĞƌŶŵĞŶƚĂů WĂŶĞů ŽŶ ůŝŵĂƚĞ ŚĂŶŐĞ ;/WͿ͘

dŚĞ ƉĞƌŵĂĨƌŽƐƚ ƌĞŐŝŽŶĂůŝnjĂƚŝŽŶ ŵĂƉ ;WĞZDͿ ŝƐ ĂŶ ŝŶƚĞƌŶĂƚŝŽŶĂů ĞĨĨŽƌƚ ǁŝƚŚŝŶ ƚŚĞ ZE ƚŚĂƚ ĂŝŵƐ ƚŽ ŝĚĞŶƚŝĨLJ ĂŶĚ ĐŚĂƌĂĐƚĞƌŝnjĞ ƚŚĞ ŬĞLJ ĞŶǀŝƌŽŶŵĞŶƚĂů ĐŽŶƚƌŽůƐ ŽŶ ĐĂƌďŽŶ ǀƵůŶĞƌĂďŝůŝƚLJ ĂŵŽŶŐ ĚŝĨĨĞƌĞŶƚ ŐĞŽŐƌĂƉŚŝĐ ƌĞŐŝŽŶƐ ĂĐƌŽƐƐ ƚŚĞ ŶŽƌƚŚĞƌŶ ƉĞƌŵĂĨƌŽƐƚ ĚŽŵĂŝŶ͘ dŚĞ ŵĂƉ ǁĂƐ ĚĞǀĞůŽƉĞĚ ďĂƐĞĚ ŽŶ ƚŚĞ ĐŝƌĐƵŵͲĂƌĐƚŝĐ ƉĞƌŵĂĨƌŽƐƚ ĂŶĚ ŐƌŽƵŶĚ ŝĐĞ ĐŽŶĚŝƚŝŽŶ ŵĂƉ ďLJ ƌŽǁŶ Ğƚ Ăů͘ ;ϭϵϵϳͿ ĂŶĚ ĐŝƌĐƵŵͲĂƌĐƚŝĐ ǀĞŐĞƚĂƚŝŽŶ ŵĂƉ ĚĞǀĞůŽƉĞĚ ďLJ tĂůŬĞƌ Ğƚ Ăů͘ ;ϮϬϬϱͿ͘ dŚĞ ƉĞƌŵĂĨƌŽƐƚ ƌĞŐŝŽŶƐ ǁĞƌĞ ĚĞĨŝŶĞĚ ƵƐŝŶŐ ĚŝĨĨĞƌĞŶƚ ƉĂƌĂŵĞƚĞƌƐ ŝŶĐůƵĚŝŶŐ ƚŽƉŽŐƌĂƉŚLJ͕ ŐĞŽŐƌĂƉŚŝĐĂů ůŽĐĂƚŝŽŶƐ ;ĐŽŶƚŝŶĞŶƚĂůŝƚLJͿ͕ ƚLJƉĞƐ ŽĨ ƉĞƌŵĂĨƌŽƐƚ ƉƌĞƐĞŶƚ͕ ƚLJƉĞƐ ŽĨ ďŝŽŵĞƐ ĂŶĚ ĂƌĐƚŝĐ ďŝŽĐůŝŵĂƚŝĐ njŽŶĞƐ ĂŶĚ ƉƌĞĚŽŵŝŶĂŶƚ ƚĞƌƌĂŝŶ ƚLJƉĞƐ͘ dŚĞ WĞZD ǁŝůů ŚĂǀĞ ŵĂŶLJ ƵƐĞƐ ĨŽƌ ƚŚĞ ƌĞƐĞĂƌĐŚĞƌƐ ǁŝƚŚŝŶ ƚŚĞ ZE ĂŶĚ ďĞLJŽŶĚ ʹ ŝŶĐůƵĚŝŶŐ ĚĂƚĂ ƐLJŶƚŚĞƐŝƐ͕ ŵŽĚĞůͲĚĂƚĂ ŝŶƚĞŐƌĂƚŝŽŶ ĂŶĚ ŵŽĚĞů ďĞŶĐŚŵĂƌŬŝŶŐ ʹ ƚŽ ĐŽŶƚƌŝďƵƚĞ ƚŽ ĂŶ ŝŵƉƌŽǀĞĚ ƵŶĚĞƌƐƚĂŶĚŝŶŐ ŽĨ ƚŚĞ ǀƵůŶĞƌĂďŝůŝƚLJ ŽĨ ƚŚĞ ƉĞƌŵĂĨƌŽƐƚ ĐĂƌďŽŶ ƉŽŽů ƚŽ ĐůŝŵĂƚĞ ĐŚĂŶŐĞ ĂŶĚ ƚŚĞ ŝŵƉůŝĐĂƚŝŽŶƐ ƚŽ ƚŚĞ ŐůŽďĂů ĐĂƌďŽŶ ďƵĚŐĞƚ͘

WĞZD ZĞŐŝŽŶĂůŝnjĂƚŝŽŶ ƌŝƚĞƌŝŽŶ dŚĞƚĂďůĞƚŽƚŚĞƌŝŐŚƚŚĂŶĚƐŝĚĞƐŚŽǁƐƚŚĞ ĐŽŶĚŝƚŝŽŶƐ ďĂƐĞĚ ŽŶ ǁŚŝĐŚ ƚŚĞ ƉĞƌŵĂĨƌŽƐƚ ƌĞŐŝŽŶƐ ǁĞƌĞ ĚĞĨŝŶĞĚ͘ dŚĞ ƉĂƌĂŵĞƚĞƌƐ ŝŶĐůƵĚĞĚ ƚŽƉŽŐƌĂƉŚLJ͕ ŐĞŽŐƌĂƉŚŝĐĂů ůŽĐĂƚŝŽŶƐ ;ĐŽŶƚŝŶĞŶƚĂůŝƚLJͿ͕ ƚLJƉĞƐ ŽĨ ƉĞƌŵĂĨƌŽƐƚ ƉƌĞƐĞŶƚ͕ ƚLJƉĞƐ ŽĨ ďŝŽŵĞƐ ĂŶĚ ĂƌĐƚŝĐ ďŝŽĐůŝŵĂƚŝĐ njŽŶĞƐ ĂŶĚ ƉƌĞĚŽŵŝŶĂŶƚ ƚĞƌƌĂŝŶ ƚLJƉĞƐ ĂƐ ƐŚŽǁŶ ŝŶ ĚŝĨĨĞƌĞŶƚ ĐŽůƵŵŶƐ ĨƌŽŵ ůĞĨƚ ƚŽ ƌŝŐŚƚ ŝŶ ƚŚĞ ƚĂďůĞ͘

PeRM: A Permafrost Regionalization Map for studying vulnerability of permafrost carbon

Santonu Goswami1 ([email protected]), Daniel J. Hayes1([email protected]), Peter Kuhry2, Gustaf Hugelius2, David McGuire3, Edward Schuur4

1Oak Ridge National Laboratory, 2Stockholm University, 3University of Alaska at Fairbanks, 4University of Florida

Increased thawing of the permafrost carbon pool in periglacial terrain due to warming, and the resulting microbial decomposition of this frozen organic carbon, is expected to be a significant positive feedback on future greenhouse gas forcing from terrestrial ecosystems to the Earth’s atmosphere. To need improve our understanding of permafrost carbon vulnerability and feedbacks is increasingly becoming a research priority for the Earth System Modeling community. Sponsored by the National Science Foundation, the “Vulenerability of Permafrost Carbon” Research Coordination Network (RCN) is a collaboration among scientists working to synthesize and link existing research about permafrost carbon and climate in a framework that will inform biospheric and climate model development, and contribute to future assessments of the Intergovernmental Panel on Climate Change (IPCC).

The permafrost regionalization map (PeRM) is an international effort within the RCN that aims to identify and characterize the key environmental controls on carbon vulnerability among different geographic regions across the northern permafrost domain. The map was developed based on the circum-arctic permafrost and ground ice condition map by Brown et al. (1997) and circum-arctic vegetation map developed by Walker et al. (2005). The permafrost regions were defined using different parameters including topography, geographical locations (continentality), types of permafrost present, types of biomes and arctic bioclimatic zones and predominant terrain types. The PeRM will have many uses for the researchers within the RCN and beyond – including data synthesis, model-data integration and model benchmarking – to contribute to an improved understanding of the vulnerability of the permafrost carbon pool to climate change and the implications to the global carbon budget.

•Lower streams of Indigirka composite geological section of about 30 m of permafrost

•Distribution of CH4 (left) and CO2 (right) through the section

•With rare exclusions there is a lack of CO2 and CH4 in fluvial deposits of Glacial Stages

•Highest concentrations of CO2 and CH4 is found in thermokarst deposits of Interstadials

•Methane is of biological origin, according to isotopic studies

•Assessment have been made for the territory of 150 000 km2 and 25 m of variable permafrost composition on the geological basis of averaging of specific contents of methane (more than 500 samples of permafrost methane were taken into account).

•The methane from permafrost could be very reactive.

Gleb Kraev, PhD, Senior Researcher at

Center for Ecology and Productivity of Forests, Russian Academy of Sciences (CEPF RAS)

Faculty of Biology, Lomonosov Moscow State University

[email protected]

on behalf of the working group lead by DSc, Prof. Dmitry Zamolodchikov.

Main research activity is:

Assessment of Biospheric Functions of Arctic Ecosystems and Boreal Forests under Global Change

1. Development of the Framework for the Forest Carbon Budget Assessments in Russia. It is based on several databases developed in the research group based on analyzes of literature and unpublished materials: the Forest Productivity, the Deadwood, the Litter, and the Soil Databases. All cover Russia and CIS area. Assimilating the official information on forest inventory, forest state monitoring, and MODIS- based forest fire, the group develops the basis of the Federal Reports on carbon emissions for the UNFCCC. Modeling of the forest dynamics is based mainly on two modeling frameworks: First developed in CEPF , and second is CBM-CFS, with Russian interface, and parameters, conversion coefficients, and regressions adapted for Russian conditions from the databases available. 2. Development of the theoretical framework for modeling of the Acrctic and boreal ecosystems functioning. Our current goal is to develop the techniques to assess and forecast all the biospheric functions of forests. We currently succeed with carbon budget accounting, and taking approaches to switch to water cycle. 3. Permafrost occupies more than 50% of Russian territory, so overlooking it lead to higher uncertainties. This is why the active layer and cryogenic processes are paid attention. Carbon budget of tundra was historically the area of the expertise of the members in the group. Aboveground, belowground fluxes, and biotic and abiotic controls over them are studied. The largest amounts of such data exist for stationers in Chukotka, and Pechora River basin. 4. The same range of study objectives, and methods applied have led the group to research activities in Antarctica with the Russian Antarctic Expedition.

I joined the laboratory at the stage of completion of my thesis entitled “Methane distribution in permafrost of North Eastern Siberia, estimation of its storage and forecast of emission from permafrost under global change”, which have been prepared in the laboratory of DSc. David Gilichinsky. The primary area of expertise was the biological challenges of the deeper layers of permafrost. Most of studies are based on permafrost drilling with lightweight mobile driller, and implementation of the new methods of laboratory biological studies to the frozen cores. Robust material exists on carbon, methane, and carbon dioxide concentrations in permafrost of the North- East Siberia. Soils, and the pedogenic processes were also studied in detail.

I also the member of Permafrost Young Researcher Network, which in Russia is well distributed across the territory. The mission of our youth society is to have expertise in the issues related to permafrost, and the regions where it could be met, to accumulate and systematize knowledge on permafrost for consulting purposes for all parties interested in this. Having a vast geography of the fieldworks (10 members have visited more than 30 locations in permafrost zone last year, ranging from Yakutia, and Chukotka, and Kamchatka, to the Baikal Region, Taymyr, Yamal, Western Siberia, European part of Russia, and Kola peninsula). This looks like the attractive perspective to use this extensive network for sample collection, spending the least for transportations. Mark J. Lara University of Texas at El Paso Department of Biological Sciences: Ecology and Evolutionary Biology

Research Summary

My PhD work has focused on understanding the functional (biogeochemical processes) implications of decade time scale plant community and environmental change at sites in northern Alaska (i.e. Barrow and Atqasuk) and Canada (i.e. central Baffin Island). With the development of multivariate-geostatistical functional models, we link structure and function, used to determine decadal functional change (see Lara et al. 2012). Recent experimental evidence, which I will be presenting in session “Vulnerability of Permafrost Carbon to Climate Change III Posters C13F”, determines the potential functional implications of 40+ years of macrophyte species increase (Villarreal et al. 2012) and elevated nitrogen and phosphorus levels in the Barrow area, thought to be associated with permafrost thaw in aquatic tundra environments. Additionally, in March 2013, I will be starting a post-doctoral position in Dr. Dave McGuire’s lab.

Relevant Publications Lara MJ, Villarreal S, Johnson DR, Hollister RD, Webber PJ, Tweedie CE. 2012. Estimated change in tundra ecosystem function near Barrow, Alaska between 1972 and 2010. Environmental Research Letters 7:015507, doi:10.1088/1748-9326/7/1/015507

Villarreal S, Johnson DR, Lara MJ, Hollister RD, Webber PJ, Tweedie CE. 2012. Tundra Vegetation change near Barrow, Alaska (1972-2010). Environmental Research Letters. 7:015508, doi:10.1088/1748-9326/7/1/015508

Elmendorf SC, Henry GHR, Hollister RD, Björk RG, Boulanger-Lapointe N, Cooper EJ, Cornelissen JHC, Day TA7, Dorrepaal E, Elumeeva TG, Gill M, Gould WA, Harte J, Hik DA, Hofgaard A, Johnson DR, Johnstone JF, Jónsdóttir IS, Jorgenson JC, Klanderud K, Klein JA, Koh S, Kudo G, Lara MJ, Lévesque E, Magnússon B, May JL, Mercado J, Michelsen A, Molau U, Myers-Smith IH, Oberbauer SF, Onipchenko VG, Rixen C, Schmidt NM, Shaver GR, Spasojevic MJ, Þórhallsdóttir ÞE, Tolvanen A, Troxler T, Tweedie CE, Villareal S, Wahren CH, Walker X, Webber PJ, Welker JM, Wipf S. 2012. Plot-scale evidence of tundra vegetation change and links to recent summer warming. Nature Climate Change 2: doi: 10.1038/NCLIMATE1465

Johnson DR, Lara MJ, Shaver GR, Batzli GO, Shaw JD, Tweedie CE. 2011. Exclusion of brown lemmings reduces vascular plant cover and biomass in arctic coastal tundra resampling of a 50+ year herbivore exclosure experiment near Barrow, Alaska. Environmental Research Letters 6:045507, doi:10.1088/1748- 9326/6/4/045507

Callaghan TV, Tweedie CE, Åkerman J, Andrews C, Bergstedt J, Butler MG, Christensen TR, Cooley D, Dahlberg 8'DQE\5.'DQLɺOV)-$GH0ROHQDDU-*'LFN-0RUWHQVHQ&((EHUW-May D, Emanuelsson U, Eriksson H, Hedenås H, Henry GHR, Hik DS, Hobbie JE, Jantze EJ, Jaspers C, Johansson C, Johansson M, Johnson DR, Johnstone JF, Jonasson C, Kennedy C, Kenney AJ, Keuper F, Koh S, Krebs CJ, Lantuit H, Lara MJ, Lin D, Lougheed VL, Madsen J, Matveyeva N, McEwen DC, Myers-Smith IH, Narozhniy YK, Olsson H, Pohjola VA, Price LW, Rigét F, Rundqvist S, Sandström A, Tamstorf M, Bogaert RV, Villarreal S, Webber PJ, Zemtsov VA. 2011 Multi-decadal changes in tundra environments and ecosystems: synthesis of the International Polar Year-Back to the Future Project (IPY-BTF) Ambio 40 705–16  1
,
0%)),:
-,+&+$
,+
:/*&+$
+
1%:&+$
,
-/*/,01
&+
*&+/)G,/
-)00@
,)
/(@
7(,+@
+
+1,+&
C
:(,:&=O@
/+
1=)*8))/P
+
%/,+
C
*&1%Q
ROGNRNO
O-/1*+1
,
,$/-%<@
+&9/0&1<
,
4:@
4:@
+A
):(,:&J7,4:C
P-/1*+1
,
,0&+0@
+&9/0&1<
,
0),@
,/:
)
%
)*#
-*
%&/
''()
*&

* / %
,/%,)

 
"&' &-!(' ,#
%*(%##1
-
*&
/($ %
7)
 -(
<8
-)
1
!%*
'&%)

GPCP
GQCT
GRCQ
GRCN
GRCS
GPCN
7)
 -(
;85
78
) )+. *1
*&$&($
(&$
;99?5
78
) )+. *1
1/
,&0
&+
+,/1%/+
/$&,+0
+
:/*
7+/)<&+$
$/,7+
<
09/)
$/0
N
/*/,01
OS
*&$&($
(&$
;9::
7%&*
)%
&
'(&#
&%
(  *
%
) 
)0&70
,*-/
1,
'+1
1//&+
H %+/7%
*
#5@
OWTPIC
%+
-
:1/
-)
#*(&)
&-#
%&*

%)(*
%*&
%
 %
* *
/)
,&0
,9/)&
-/*/,01
1%<
/
707))<
00,&1
:&1%
1)&(0C
+
&G/&%
ON
+/,=+
'()%*85
78
 /
(&$
* 
)&-*
#&%
* 
() )+. *1
# %
%
1
;99?5
78
 /
(&$
* 
%&(*
) &/ %
%/
'&% %
%
--)*
-/*/,01
1//&+@
1%
$/,:1%
,
1)&(0
*<
/07)1
&+
1%:
04)*+1
+
1%
S
9),-*+1
,/
+)/$*+1
,
1%/*,(/01
-,+0C
9/)
017&0
%9
+
;9:95
&*4
* 
&-%(1
*/%
(&2%
%
-%(&2%
(&-%
7+/1(+
&+
,+2+7,70
-/*/,01
1,
*07/
,/
*,)
1%0
&*-10
HC$C
7/+@
N
7) 
# %
%
*&$&($)8
)
*
&-*
<99
& $6$5
PNNSA
&0,/,7$%@
PNNTI@
71
1%/
/
:
*07/*+10
,
1%
 1
,
0%)),:
GS
)0
PS



:1/
,&0
,+
$/,7+
1*-/17/0
&+
1%
&0,+2+7,70
-/*/,01
=,+C
%&)
GON
,/%,)
07%
-,+0
*<
,*
)00
,**,+
&+
-1)+0
7+/
)&*1
:/*&+$
H*&1%
*
#5@
PNNSI
1%<
,7)
-/,*,1
1%:
,
/,=+
$/,7+
,+

1/+0&+1
0&0
+
GOS
/*/,01
,+0.7+1)<
 1
/,+
/)0C

GPN
 


 
" 
#
+/,=+
,
;*&+
1%
&*-1
,
0%)),:
-,+&+$
H +
0
:1/
,&0
:%&%
/=
GPS
1%/,7$%
&+
:&+1/I@
1*-/17/0
&+
,/%,)0
%9
+
*,+&1,/
0&+
PNNQ
&+
PNNT
PNON

09/)
*&+/)G,/
-)00
H-/*/,01G,/
*,7+0I
),1
,+

9))<
",,/
1
OTN
NOFNWFNR
NOFNOFNS
NOFNSFNS
NOFNWFNS
NOFNOFNT
NOFNSFNT
NOFNWFNT
NOFNOFNU
NOFNSFNU
NOFNWFNU
NOFNOFNV
NOFNSFNV
NOFNWFNV
NOFNOFNW
NOFNSFNW
NOFNWFNW
NOFNOFON
NOFNSFON
NOFNWFON
NOFNOFOO
NOFNSFOO
1/)&+
&+
1%
,)
/(
0&+
+/
%&1%,/0@
7(,+
HTNCS,
OQSCP,IC
%
NCS
-/*/,01
&+
1%
-)00
&0
YON
*
1%&(@
'701
),:
NK@
+
*,/1)<
&G/&%
N
 -(
?5
&$'( )&%
&
H,7)20%
+
:(,:&=@
PNNQA
:(,:&=
+
,7)20%@
PNNRA
:(,:&=
*
#5@
(&-%
%
/*(
ON
GNCS
V
PNOOIC
%
-)00
%9
+
 1
<
%+$0
&+
:1/
)9)0
,9/
1%
-01
V
</0
NOFOPFNQ
NOFNSFNR
NOFONFNR
NOFNQFNS
NOFNVFNS
NOFNOFNT
NOFNTFNT
NOFOOFNT
NOFNRFNU
NOFNWFNU
NOFNPFNV
NOFNUFNV
NOFOPFNV
NOFNSFNW
NOFONFNW
NOFNQFON
NOFNVFON
NOFNOFOO
*$'(*-()
*
(&- #1
N
N
* 
)$
'*
/ * %
T
)),:&+$
;*&+2,+
,
1%
&*-1
,
+:
-,+&+$
,+
-/*/,01C
%
/07)10
GO
GON
%
!%*
*&
#)
;<3
R
-/0+1
%/
,70
,+
-)0
PS
H01)
7+2)
/+1)

 

" #
* %
#
* 
1(
(&$
+7)
1*-/17/
*07/*+10
&+
1%
,/%,)0
:/
*
1
)01
++7))<
<
GUN
NOFNOFNS
NOFNSFNS
NOFNWFNS
NOFNOFNT
NOFNSFNT
NOFNWFNT
NOFNOFNU
NOFNSFNU
NOFNWFNU
NOFNOFNW
NOFNSFNW
NOFNWFNW
NOFNOFON
NOFNSFON
NOFNWFON
NOFNOFOO
NOFNSFOO
NOFNWFNR
NOFNOFNV
NOFNSFNV
NOFNWFNV
&.$(
:>
*&
1
;>5
),:/&+$

1%/*&01,/
&+0&
1%
0&+$
+
1(&+$
/&+$0
1
%
)9)
,/

 -(
;5
##1
%
&#
("3
:>
"$
)&-* )*
&
GVN
OPFNWFNW
OPFONFNW
OPFOOFNW
OPFOPFNW
OPFNOFON
OPFNPFON
OPFNQFON
OPFNRFON
OPFNSFON
OPFNTFON
OPFNUFON
OPFNVFON
O
*
-1%
OCS
*
-1%
P
*
-1%
QCVS
*
-1%
S
*
-1%
TCVS
*
-1%
*(
'*
)
*&+&*7*
,
S
*&+710
&+
,//
1,
02*1
1%
.7&)&/&7*
1*-/17/
H7/<
 * &()3
) &/ %
 %)
%
( %
%
'&% %
)0
PQ
)0
PS
''(&0 $*
%
$1
/,7+
1*-/17/
OPN
*
-1%
1/
1*-/17/
OPN
*
-1%
[NCP,IC
+
-)0
:0
&+01/7*+1
:&1%

1%/*&01,/
01/&+$
+
+

1G),$$/
(&$
;99=
7#8
*&
;9:9
7(  *8
-
*&
.(
$
 -(
<5
&%* #1
.(
 (
*$'(*-(
%
$%
%%-#
 (
 -(
>5
-(
),#$%*
*
&( &#)
%
#)
;<
%
.
%()
(&$
;99B
7(68&%)*(-+&%5
( %
)
)&-* /()
7*&/()
* 
*$'(*-(
7 8
$)-(
*
#)
B
7-''(
('
%
*#8
%
, #)
;>3
;99<6;9::5

*&
;9:95

H7/<
[NCO I
+
%0
-/,9&
,+2+7,70
1
0&+
PNNRC
%
/*&+/
:/
&,&$
&
* 
$)8
%
$)
(
# %(
*-()
&%
(&-%
*$'(*-(
$)-(
*
)#*
'* )
%
#)
;>
7#&/(
&+01/7*+1
&+
PNNW
:&1%
,,
/,
1G),$$/0
+
;1/+)
1%/*&01,/0
H7/<
* 
&/%.##1
) )
&
* 
'&%)5
$
)&-(4
(' 83
;99=6;9::5
&*
* 
$-
/($(
*$'(*-()
%
/ %*(
 -(
@5
(&()) .
, [NCP IC
&/
1*-/17/0
&+
1%
9))<
%9
)0,
+
/,/
0&+
PNNO
70&+$

&&#
(* 5
;9::
/ %
#$&)*
* 
%+(
&( &#
C:
$
'*
/)
)&* ($#5
 %
%
#)
;<


+
)0
PQ
,/%,)
0%&)
1%/*&01,/
4%
1,

,,
/,
),$$/C
*/%
;99?
%
,/%,)

;9::5
78
%
784
)1/&)
/0&029&1<
1,*,$/-%<
HI
:0
70
1,
;*&+
/,=+
$/,7+
,+&2,+0


  /*/,01
 &*&)
&
)*(%
%
:&1%&+
1%
-)00C
%
!<
,
1%
*1%,
&0
0
,+
1%
,+1/01
&+
1%
)1/&)
9/
*0
:/
))
/%
1
1%
01/1
,
1%
017<
+
1%/
:0
)&4)
01+&+$
:1/
H&$7/
PIC
%
*0
&
* 
'#)
) &/ %
+/,=+
0
,+
,+729&1<
H/0&029&13
;99

My interests lie in permafrost distribution, characteristics and dynamics. In recent years, we have worked mainly in the discontinuous permafrost zone of the southern Yukon and northern British Columbia. We have measured ground and air temperatures, modeled the current permafrost distribution, provided first order estimates of the impact of future climate change on that distribution, and we are monitoring thin permafrost sites along the Alaska highway for signs of thaw using geophysical techniques. We are collaborating with two UK-based groups examining carbon release along south-north transects in western Canada. We are about to start a new permafrost modeling program in eastern Labrador, also in warm, discontinuous permafrost. DŽĚĞůŝŶŐƉĞƌŵĂĨƌŽƐƚĐĂƌďŽŶĐLJĐůĞƵŶĚĞƌĐůŝŵĂƚĞǁĂƌŵŝŶŐ͕ƌŝƐŝŶŐĂƚŵŽƐƉŚĞƌŝĐKϮ ĂŶĚĂůƚĞƌĞĚƉƌĞĐŝƉŝƚĂƚŝŽŶ ĂƚĂƚƵŶĚƌĂƐŝƚĞ :ŝĂŶǁĞŝ>ŝϭ͕^ƵƐĂŶEĂƚĂůŝϮ͕ϰ͕ĚǁĂƌĚ'^ĐŚƵƵƌϮ͕:ŝĂŶLJĂŶŐyŝĂϭ͕ĞƌŶĂƌĚWĂŬϯ͕zŝŶŐƉŝŶŐtĂŶŐϯ͕zŝƋŝ>ƵŽϭ ϭ͘ĞƉĂƌƚŵĞŶƚŽĨŽƚĂŶLJĂŶĚDŝĐƌŽďŝŽůŽŐLJ͕dŚĞhŶŝǀĞƌƐŝƚLJŽĨKŬůĂŚŽŵĂ͕EŽƌŵĂŶ͕K<͕hŶŝƚĞĚ^ƚĂƚĞƐ͘ Ϯ͘ĞƉĂƌƚŵĞŶƚŽĨŝŽůŽŐLJ͕hŶŝǀĞƌƐŝƚLJŽĨ&ůŽƌŝĚĂ͕'ĂŝŶĞƐǀŝůůĞ͕&>͕hŶŝƚĞĚ^ƚĂƚĞƐ͘ ϯ͘^/ZKDĂƌŝŶĞĂŶĚƚŵŽƐƉŚĞƌŝĐZĞƐĞĂƌĐŚ͕ĞŶƚƌĞĨŽƌƵƐƚƌĂůŝĂŶtĞĂƚŚĞƌĂŶĚůŝŵĂƚĞZĞƐĞĂƌĐŚ͕ƐƉĞŶĚĂůĞ͕sŝĐƚŽƌŝĂ͕ƵƐƚƌĂůŝĂ ϰ͘ƵƌƌĞŶƚůLJĂƚtŽŽĚƐ,ŽůĞZĞƐĞĂƌĐŚĞŶƚĞƌ͕&ĂůŵŽƵƚŚ͕D͕hŶŝƚĞĚ^ƚĂƚĞƐ

/ŶƚƌŽĚƵĐƚŝŽŶ ZĞƐƵůƚƐ ZĞƐƵůƚƐ (W) (SW)(WW) (C) (WXC) /ŶĂƚƵŶĚƌĂƐŝƚĞŶĂŵĞĚŝŐŚƚDŝůĞ>ĂŬĞ;D>ͿĂƚůĂƐŬĂ;EĂƚĂůŝ ĞƚĂů͕ *URVVSULPDU\SURGXFWLYLW\ *URVVSULPDU\SURGXFWLYLW\ *URVVSULPDU\SURGXFWLYLW\ *URVVSULPDU\SURGXFWLYLW\ *URVVSULPDU\SURGXFWLYLW\ *URVVSULPDU\SURGXFWLYLW\      

 

 

     ϮϬϭϭ͕ϮϬϭϮͿ͕ƚŚĞŽŵŵƵŶŝƚLJƚŵŽƐƉŚĞƌĞŝŽƐƉŚĞƌĞ>ĂŶĚdžĐŚĂŶŐĞ          

GD\  GD\ GD\  

 GD\ 

GD\      ŵŽĚĞů;>ͿǁĂƐĐĂůŝďƌĂƚĞĚĂŶĚǀĂůŝĚĂƚĞĚƚŽƐŝŵƵůĂƚĞĐĂƌďŽŶĐLJĐůĞ           

            ƵŶĚĞƌĐůŝŵĂƚĞǁĂƌŵŝŶŐ͕ƌŝƐŝŶŐĂƚŵŽƐƉŚĞƌŝĐKϮ͕ĂŶĚĂůƚĞƌĞĚ            ƉƌĞĐŝƉŝƚĂƚŝŽŶĂƐƐŝŶŐůĞĨĂĐƚŽƌŽƌŝŶĐŽŵďŝŶĂƚŝŽŶ ĚƵƌŝŶŐϮϬϬϳͲϮϬϭϬ͘      &DUERQIOX[ J&P &DUERQIOX[ J&P &DUERQIOX[ J&P &DUERQIOX[ J&P    &DUERQIOX[ J&P    - - - - - - - - - - - - - - - 6 6 6 6 6 6 6 6 - - - - - $ ' ' ' ' 6 ' 6 ' 6 ' 6 ' 6 6 6 6 - - - - - $ $ $:6:::&:;& 0 0 0 ' 0 ' 0 ' 0 ' ' ' ' ' $ 0 0 0 0 0 0 6 6 6 6 $ ' ' ' ' 0 0 ^ŝƚĞŚĂƌĂĐƚĞƌŝƐƚŝĐƐ 0 (FRV\VWHPUHVSLUDWLRQ (FRV\VWHPUHVSLUDWLRQ (FRV\VWHPUHVSLUDWLRQ (FRV\VWHPUHVSLUDWLRQ (FRV\VWHPUHVSLUDWLRQ (FRV\VWHPUHVSLUDWLRQ      

                 GD\ GD\ GD\ GD\ GD\

            

              b  &DUERQIOX[ J&P &DUERQIOX[ J&P &DUERQIOX[ J&P &DUERQIOX[ J&P    &DUERQIOX[ J&P    $:6:::&:;& - - - - - 6 6 6 6 - - - - - - - - - - - - - - - c $ ' ' ' ' 6 6 6 6 - - - - - $ 0 0 0 ' ' ' ' 6 6 6 6 6 6 6 6 $ $ 0 0 0 ' ' ' ' ' ' ' ' 6 6 6 6 $ 0 0 0 0 0 0 ' ' ' ' 0 0 WŚŽƚŽƐďLJ^ƵƐĂŶEĂƚĂůŝ 0 ĂΘď͘>ŽĐĂƚŝŽŶŽĨƚŚĞĂƌďŽŶŝŶWĞƌŵĂĨƌŽƐƚdžƉĞƌŝŵĞŶƚĂů,ĞĂƚŝŶŐZĞƐĞĂƌĐŚ;ŝW,ZͿ a 1HWHFRV\VWHPH[FKDQJH 1HWHFRV\VWHPH[FKDQJH 1HWHFRV\VWHPH[FKDQJH 1HWHFRV\VWHPH[FKDQJH 1HWHFRV\VWHPH[FKDQJH 1HWHFRV\VWHPH[FKDQJH Đ͘^ŶŽǁĨĞŶĐĞƐǁĞƌĞƵƐĞĚƚŽǁĂƌŵƐŽŝůƚĞŵƉĞƌĂƚƵƌĞƐŝŶǁŝŶƚĞƌ      

 

 

       DŽĚĞůĚĞƐĐƌŝƉƚŝŽŶ       GD\

   GD\  GD\ GD\ GD\

             DĂƚŚĞŵĂƚŝĐĂůůLJ͕ƚŚĞĐĂƌďŽŶŵŽĚĞůŝƐŐŝǀĞŶďLJ  dX (t)  = BU (t) − ξ (t) ACX (t ) ƚŚĞĨŝƌƐƚŽƌĚĞƌŽƌĚŝŶĂƌLJĚŝĨĨĞƌĞŶƚŝĂůĞƋƵĂƚŝŽŶ͘    

  dt        X (0) = X      0       &DUERQIOX[ J&P

&DUERQIOX[ J&P  &DUERQIOX[ J&P &DUERQIOX[ J&P >ĞĨƚĨŝŐƵƌĞĚĞƉŝĐƚƐƚŚĞƚĞƌƌĞƐƚƌŝĂůĐĂƌďŽŶĐLJĐůĞ͘   &DUERQIOX[ J&P    $:6:::&:;& - - - - - 6 6 6 6 $ ' ' ' ' - - - - - 0 0 0 6 6 6 6 $ ' ' ' ' - - - - - - - - - - - - - - - 0 0 0 6 6 6 6 6 6 6 6 6 ' 6 ' 6 ' 6 ' $ $ ' ' ' ' ' 0 ' 0 ' 0 ' $ 0 0 0 0 0 0

Gross primary productivity Ecosystem respiration Net ecosystem exchange GPP, Reco, NEE at Ambient condition Gross primary productivity Ec os y s tem respiration Net ecosystem exchange GPP, Reco, NEE at Ambient condition

ZŝŐŚƚĨŝŐƵƌĞŝůůƵƐƚƌĂƚĞƐƚŚĞĨůŽǁŽĨŵŽĚƵůĞ (RFSF0.5) (RFSF1.5) ZĞƐƵůƚƐƐŚŽǁĞĚ;ϭͿǁŝŶƚĞƌǁĂƌŵŝŶŐŝŶĚƵĐĞĚĂŐƌĞĂƚĞƌƉŽƐŝƚŝǀĞĞĨĨĞĐƚŽŶŶĞƚĞĐŽƐLJƐƚĞŵĐĂƌďŽŶĞdžĐŚĂŶŐĞƚŚĂŶ ĚĞƉĞŶĚĞŶĐLJŝŶ>͘ĂĐŚĚĂƐŚĞĚƌĞŐŝŽŶ *URVVSULPDU\SURGXFWLYLW\ *URVVSULPDU\SURGXFWLYLW\ *URVVSULPDU\SURGXFWLYLW\ ƐƵŵŵĞƌǁĂƌŵŝŶŐ;ϭϰйǀƐ͘ϯйͿŝŶĐŽŵƉĂƌŝƐŽŶƚŽĂŵďŝĞŶƚƚĞŵƉĞƌĂƚƵƌĞ͖;ϮͿƌŝƐŝŶŐĂƚŵŽƐƉŚĞƌŝĐKϮ͕ĂŶĚŝƚƐ ƌĞƉƌĞƐĞŶƚƐĂŵŽĚƵůĞ͘ŶLJƐƵďƌŽƵƚŝŶĞƐ   ĐŽŶƚĂŝŶĞĚǁŝƚŚŝŶĂŵŽĚƵůĞĂƌĞƐŚŽǁŶŝŶďŽůĚ  ŝŶƚĞƌĂĐƚŝŽŶǁŝƚŚǁĂƌŵŝŶŐŐƌĞĂƚůLJŝŶĐƌĞĂƐĞĚƚŚĞŶĞƚĞĐŽƐLJƐƚĞŵĐĂƌďŽŶĞdžĐŚĂŶŐĞƵƉƚŽϮϯйĂŶĚϰϭй͕ƌĞƐƉĞĐƚŝǀĞůLJ͖       ƚLJƉĞďĞůŽǁƚŚĞŵŽĚƵůĞŶĂŵĞ͘dŚĞŵĂŝŶ    ĂŶĚ;ϯͿŝŶĐƌĞĂƐĞĚƉƌĞĐŝƉŝƚĂƚŝŽŶŚĂƐĂŶĞŐĂƚŝǀĞĞĨĨĞĐƚŽŶŶĞƚĞĐŽƐLJƐƚĞŵĐĂƌďŽŶĞdžĐŚĂŶŐĞĚƵĞƚŽŵĂŝŶůLJƐŶŽǁĨĂůů ƐƵďƌŽƵƚŝŶĞ͞Đďŵ͟ŝƐƐŚŽǁŶŝŶŐƌĞĞŶ͖ GD\  GD\       ZŽƵŐŚŶĞƐƐ͕ƌĂĚŝĂƚŝŽŶ͕ĐĂŶŽƉLJ͕ƐŽŝůĂŶĚƐŶŽǁ    ĐŽǀĞƌ͖  

  ƌŽƵƚŝŶĞƐĂƌĞĐĂůůĞĚĨƌŽŵŝƚ͘;ĚĂƉƚĞĚĨƌŽŵ͞   ƵƐĞƌŐƵŝĚĞĨŽƌƚŚĞ^^ůĂŶĚƐƵƌĨĂĐĞŵŽĚĞů    ŽŵŵƵŶŝƚLJƚŵŽƐƉŚĞƌĞŝŽƐƉŚĞƌĞ>ĂŶĚ    ŝƐĐƵƐƐŝŽŶƐ džĐŚĂŶŐĞ;>Ϳ͟ďLJ'͘ďƌĂŵŽǁŝƚnj͕zW   6LPXODWLRQV LQ tĂŶŐĂŶĚ͘WĂŬ͘Ϳ    &DUERQIOX[ J&P  &DUERQIOX[ J&P    ^ŝŵƵůĂƚŝŽŶƌĞƐƵůƚƐĂƚƚŚĞƚƵŶĚƌĂƐŝƚĞŝŶůĂƐŬĂƐƵŐŐĞƐƚĞĚ;ϭͿƚŚĞ 5)6) - - - - - - - - - - $ 5) 5) 5)6) 5)6) 6 6 6 6 6 6 6 6 $ $ ' ' ' ' ' ' ' ' 0 0 0 0 0 0 DŽĚĞůĐĂůŝďƌĂƚŝŽŶ ĞĐŽƐLJƐƚĞŵŝƐĂĐĂƌďŽŶƐŝŶŬĨƌŽŵϮϬϬϳƚŽϮϬϭϬ͖;ϮͿ 5)6)  5)6) (FRV\VWHPUHVSLUDWLRQ (FRV\VWHPUHVSLUDWLRQ (FRV\VWHPUHVSLUDWLRQ ƚŚĞLJĞĂƌůLJĂǀĞƌĂŐĞŽĨ'WW͕ĞĐŽƐLJƐƚĞŵƌĞƐƉŝƌĂƚŝŽŶĂŶĚŶĞƚĞĐŽƐLJƐƚĞŵ 6RLOWHPSHUDWXUH (FRV\VWHPUHVSLUDWLRQ      \  [  ĞdžĐŚĂŶŐĞǁĞƌĞƐĞŶƐŝƚŝǀĞƚŽƚĞŵƉĞƌĂƚƵƌĞ͕ĂƚŵŽƐƉŚĞƌŝĐKϮ



5ð     

       ĐŽŶĐĞŶƚƌĂƚŝŽŶĂŶĚƉƌĞĐŝƉŝƚĂƚŝŽŶ͖;ϯͿǁĂƌŵŝŶŐĂŶĚƌŝƐŝŶŐĂƚŵŽƐƉŚĞƌŝĐ    GD\ GD\       GD\    KϮ ĐŽŶĐĞŶƚƌĂƚŝŽŶĂůŽŶĞŽƌŝŶĐŽŵďŝŶĂƚŝŽŶĐĂŶŝŶĚƵĐĞŐƌĞĂƚĞƌ        ĞĐŽƐLJƐƚĞŵĐĂƌďŽŶŐĂŝŶĚƵĞƚŽƚŚĞŝƌŐƌĞĂƚĞƌĞĨĨĞĐƚŽŶ'WWƚŚĂŶ     6RLOWHPSHUDWXUH ƒ&          ĞĐŽƐLJƐƚĞŵƌĞƐƉŝƌĂƚŝŽŶ͖ĂŶĚ ;ϰͿƚŚĞŶĞŐĂƚŝǀĞĞĨĨĞĐƚŽĨŝŶĐƌĞĂƐŝŶŐ       5HFR J&P  ƉƌĞĐŝƉŝƚĂƚŝŽŶŽŶĞĐŽƐLJƐƚĞŵĐĂƌďŽŶŐĂŝŶĂƉƉĞĂƌĞĚĂƚƚƌŝďƵƚĂďůĞƚŽƚŚĞ 6RLOWHPSHUDWXUH ƒ&  

 &DUERQIOX[ J&P  &DUERQIOX[ J&P    ƐŚŽƌƚĞŶĞĚŐƌŽǁƚŚƚŝŵĞĚƵĞƚŽƚŚĞůĂƚĞƐƚĂƌƚŽĨŐƌŽǁŝŶŐƐĞĂƐŽŶĂƐ -  $ $ $ $ $ $ $ ' ' ' 0 $ 5) 5) 5)6) 5)6) - - - - - - - - - - - $ $ $ $ $ $ $      ' ' ' 6 6 6 6 6 6 6 6 0 $ $ ' ' ' ' ' ' ' ' 0 0 0 0  0 0 ƌĞĨůĞĐƚĞĚďLJƐŽŝůƚĞŵƉĞƌĂƚƵƌĞĂƚƚŚĞƚƵƌŶŽĨDĂLJƚŽ:ƵŶĞ;ƐĞĞƌŝŐŚƚ Simulation Observation ĨŝŐƵƌĞͿ͖ '$< 1HWHFRV\VWHPH[FKDQJH 1HWHFRV\VWHPH[FKDQJH 1HWHFRV\VWHPH[FKDQJH *URVVSULPDU\SURGXFWLYLW\ DŽĚĞůĞdžƉĞƌŝŵĞŶƚ     &ƵƚƵƌĞǁŽƌŬ     ďďƌĞǀŝĂƚŝŽŶ dƌĞĂƚŵĞŶƚĐŽŶĚŝƚŝŽŶ          GD\ GD\   AAmbient [CO2], air temperature, rainfall and snowfall   ;ϭͿ dŽǀĂůŝĚĂƚĞƚŚĞŵŽĚĞůŽŶƚŚĞŵĞƚŚĂŶĞ;, ͿĞŵŝƐƐŝŽŶďĂƐĞĚŽŶŽďƐĞƌǀĂƚŝŽŶƐĂƚƚŚĞƐŝƚĞĂŶĚƚŽƐŝŵƵůĂƚĞƚŚĞ    ϰ GD\

   W air temperature plus 2°C   ŵƵůƚŝƉůĞĐŚĂŶŐĞĨĂĐƚŽƌƐŽŶŵĞƚŚĂŶĞĞŵŝƐƐŝŽŶ͖

  SW air temperature plus 2°C only in summer     ;ϮͿ dŽĞǀĂůƵĂƚĞƚŚĞƌĞůĂƚŝǀĞŝŵƉŽƌƚĂŶĐĞŽĨKϮ ĂŶĚ,ϰ ĞŵŝƐƐŝŽŶƐŝŶƚĞƌŵƐŽĨŐůŽďĂůǁĂƌŵŝŶŐƉŽƚĞŶƚŝĂůƵŶĚĞƌ WW air temperature plus 2°C only in winter    

*33 J&P C Rising [CO2] to 700 ppmv    ŵƵůƚŝƉůĞĐůŝŵĂƚĞĐŚĂŶŐĞƐĐĞŶĂƌŝŽƐ͖ 

WXC Interaction of warming and rising [CO ] &DUERQIOX[ J&P &DUERQIOX[ J&P 2    ;ϯͿ dŽĞdžƉůŽƌĞƚŚĞŵĞĐŚĂŶŝƐƚŝĐƵŶĚĞƌƐƚĂŶĚŝŶŐŽĨƐŽŝůƚĞŵƉĞƌĂƚƵƌĞ͕ǁĂƚĞƌĐŽŶƚĞŶƚ͕ůĞĂĨƉŚĞŶŽůŽŐLJĂŶĚŽƚŚĞƌ - $ $ $ $ $ $ $ ' ' ' 0 RF0.5 rainfall 50% less than ambient $ 5) 5) 5)6) 5)6) - - - - - - - - - - 6 6 6 6 6 6 6 6 $ $ ' ' ' ' ' ' ' ' 0 0 0 0 0 0 ĐŚĂŶŐĞƐŝŶƌĞŐƵůĂƚŝŶŐƚŚĞĞĨĨĞĐƚƐŽĨŵƵůƚŝƉůĞĐůŝŵĂƚĞĐŚĂŶŐĞƐŽŶĞĐŽƐLJƐƚĞŵĐĂƌďŽŶĐLJĐůĞƐ͖ RF1.5 rainfall 50% more than ambient ¾ The correlation coefficient of observation and simulation is 0.93, 0.90,0.83 and 0.78 for soil RFSF0.5 rainfall and snowfall 50% less than ambient Gross primary productivity Ecosystem respiration Net ecosystem exchange GPP, Reco, NEE at Ambient condition temperature at 0-5, 5-10, 10-20 and 20-40cm, RFSF1.5 rainfall and snowfall 50% more than ambient 0.88 for GPP and 0.91 for ecosystem ĐŬŶŽǁůĞĚŐĞŵĞŶƚ͗ ƚŚĞƌ͘>ƵŽ͛Ɛ ĐŽůĂďŐƌŽƵƉ ĂƚƚŚĞhŶŝǀĞƌƐŝƚLJ ŽĨKŬůĂŚŽŵĂ͘ respiration; ŽŶƚĂĐƚ͗ũŝĂŶǁĞŝůŝ͘ϮΛŐŵĂŝů͘ĐŽŵ  Geodetic Studies on Surface Dynamics of Permafrost and Active Layer Thickness Lin Liu1*, Tingjun Zhang2, Kevin Schaefer2, and John Wahr3 1. Department of Geophysics, Stanford University; 2. National Snow and Ice Data Center, University of Colorado; 3 CIRES and Department of Physics, University of Colorado *Email: [email protected]

Abstract InSAR Results: Seasonal and Long-term Surface Subsidence What Causes Long-term Subsidence?

We apply interferometric synthetic aperture radar (InSAR) to measure surface deformation over permafrost on the North Slope   
  
of Alaska during the 1992-2000 thawing seasons. We find Time series of surface subsidence at Active layer If enough heat transfers through the active Betty Pingo. seasonally varying vertical displacements of 1-4 cm with Active layer layer to the underlying permafrost, ice-rich subsidence occurring during the thawing season and a long-term Ice-rich layer We simply connect adjacent thawing permafrost thaws and massive ground ice Ice-rich layer subsidence of 1-4 cm/decade. We hypothesize that the seasonal seasons with straight dotted lines. melts into liquid water. Meltwater drains into subsidence is caused by thaw settlement of the active layer and lowlands, river channels, and thaw lakes, develop a retrieval algorithm to estimate active layer thickness. We Permafrost Permafrost resulting in surface subsidence. prostitute that the secular subsidence is probably due to thawing of ice-rich permafrost near the permafrost table.

Background Implications for GPS Sites in Northern Alaska

InSAR measures changes of satellite- target distance between two acquisitions GPS site (SG27) at Barrow, AK over the same area.

1st flyoverovveer Subsidence during thaw months (June-Sept) Long-term subsidence during 1992-2000 2ndd =(4
/) R flyoverver USGS Not to scale Surface displacement Estimate active layer thickness from seasonal subsidence

Height time series at SG27  

  

The seasonal subsidence is caused by 
 

 
thaw settlement in the active layer, in The North Slope of Alaska is which pore ice melts into liquid water, underlain by continuous  
  
resulting in a volume decrease. permafrost that contains ground 
 

ice up to 70% by volume.  
Red dots are monitoring sites of active layer thickness 

  
 
 



 







 
 


 
Red dots locate permanent GPS sites in areas Long-term and seasonal subsidence due underlain by continuous and discontinuous permafrost. Top active layer thaws and to permafrost dynamics? (Data source: UNAVCO, NSIDC, NOAA) freezes annually Active layer Ice-rich layer We assume porosity decreases exponentially How GPS data can be used in InSAR studies on permafrost dynamics? Soil at or below 0 °C for at with depth, calculate the integral and then •
GPS-measured ground deformation can validate/calibrate remote sensing InSAR least two consecutive years Permafrost invert it for the active layer thickness (ALT) measurements. •
GPS-based soil moisture and snow cover measurements can help to mitigate artifacts in InSAR measurements.

How InSAR studies can help to interpret GPS data? Displacement Time Series Model •
InSAR provides a regional assessment of surface deformation related to permafrost dynamics. •
A better understanding and quantitative measurements of long-term permafrost thaw We model the vertical displacement (D) as the summation of a settlement will be helpful to separate tectonics and non-tectonics signals in GPS secular term and a seasonal term: measurements. References where R is the secular rate, t is time, E is the amplitude coefficient of the seasonal signal, and A is the accumulated degree day of thaw. InSAR-estimated ALT shows larger Liu, L., T. Zhang, and J. Wahr (2010), InSAR measurements of surface deformation over permafrost on the The square-root-of-thawing-day relation for seasonal subsidence is values over wet tundra areas than over North Slope of Alaska, J. Geophys. Res., 115, F03023, doi:10.1029/2009JF001547. based on the simplified Stefan equation. dry tundra areas. Liu, L., K.Schaefer, T. Zhang, and J. Wahr (2011), Estimating Active Layer Thickness from Remotely Sensed Surface Deformation, submitted to J. Geophys. Res.. Research Statement: Remote Sensing Permafrost Deformation Lin Liu, Department of Geophysics, Stanford University, [email protected] My research focuses on mapping and understanding surface deformation over permafrost areas using a remote sensing/geodetic technique called SAR interferometry (or InSAR). Applying this technique to radar data acquired by space-borne instruments, we are able to map the surface deformation over large area (typically 100 km by 100 km), at a high spatial resolution of about 5 m. Using InSAR, I found seasonal and long-term surface subsidence over the permafrost areas on the North Slope of Alaska. The long-term surface subsidence is caused by melting of ground ice within the permafrost associated with increased ground temperatures in the last two decades. I also developed a new method to estimate the thickness of the active layer using the InSAR-measured seasonal surface subsidence, which greatly expands the spatial coverage of the traditional field-based observations. My ongoing studies expand to arctic permafrost in Svalbard and near Thule in northwest Greenland, alpine permafrost in the Sierra Nevada of California, and plateau permafrost in Tibet. On the other hand, I took advantage of the high-spatial resolution of InSAR data to quantify surface motion at individual targets at scales of a few hundreds of meters, including drained thermokarst lake basins, thermokarst sites, and pingos. The permafrost carbon-climate feedback simulated by a coupled global climate model: feedback strength and sensitivity

Andrew H. MacDougall School of Earth and Ocean Sciences, University of Victoria, Victoria BC, Canada, V8W 3V6 [email protected]

Abstract Climate sensitivity Results: Emissions Pathwaysy Northern permafrost soils contain an estimated 1700 Pg of carbon, almost twice as much carbon as the contemporary atmosphere. As climate warms and permafrost Figure 3. Equilibrium change in Earth's thaws much of this carbon will decay, releasing carbon to the atmosphere leading surface temperature at a specified to further warming. Here this permafrost carbon-climate feedback is incorporated atmospheric CO2 concentration for into the UVic Earth System Climate Model (ESCM) by prescribing carbon into three climate sensitivities (to a perennially frozen soil layers. When such a layer thaws the carbon within it is doubling of CO2). transferred to the active soil carbon pool. The UVic ESCM is forced under four emissions pathways diagnosed from RCPs 2.6, 4.5, 6.0, and 8.5. The strength of the permafrost carbon feedback is estimated to be 0.25(0.1 to 0.7)oC by 2100 independent of emissions pathway followed. For simulations with a climate o sensitivity (to a doubling of CO2) above 3.0 C CO2 continues to build up in the atmosphere for the indefinite future, even if anthropogenic carbon emissions are reduce to zero by 2013. This analysis suggests that there may be very little that can be done to prevent future warming from the permafrost carbon feedback.

Introduction Results: Industrial Shutdown The UVic ESCM is a global climate model of intermediate complexity, with a fully coupled representation of oceanic and terrestrial carbon cycles. Here the frozen ground version of the UVic ESCM is augmented to include a representation of the sequestered permafrost carbon pool, in order to quantify the permafrost carbon feedback. This feedback has not been taken into account by any of the models Figure 5. Anomaly in CO2 concentration with respect to baseline runs with Figure 7. Global average surface air temperature (SAT) anomaly with assessed for the 4th or being assessed for the 5th IPCC Assessment Reports. no permafrost carbon, for each DEP. Coloured areas are the likely SAT respect to baseline runs with no carbon sequestered in permafrost soil anomaly ranges for each diagnosed emissions pathway (DEP). The median layers. Median values for each range are shown as a black line. for each DEP is superimposed as a black line. The range in outcomes for each DEP are generated by varying the model's climate sensitivity (to a

Methods doubling of CO2) and the permafrost carbon density.

Figure 1. Schaefer et al. (2011) method of transferring sequestered permafrost carbon to the active soil carbon pool. Carbon in the active layer is created and administered by the existing soil carbon model component. A threshold depth

(Tdepth), equal to the deepest historical active layer thickness (ALT), separates the active soil Figure 9. Melt-model parameter sensitivity to mean annual air temperature and permafrost carbon pools. (first row), temperature lapse rate (second and third rows) and wind speed (fourth row). Melt models use a constant lapse rate of -6.5oC km-1 in the “fixed” When the thaw depth of soil exceeds this threshold, the carbon from the lapse-rate test (second row) and are fed the lapse rate used in the IEBM in the newly thawed layers is transferred to the active soil carbon pool and the “variable” test (third row). Empirical melt models are tuned to the output of the Figure 6. Changes in the size of each Earth system carbon pool in response threshold depth is increased. Permafrost carbon is assumed to have a , the carbon from the newly thawed layers is transferred to the active soil carbon pool and EBM forced under idealized conditions. to the addition of permafrost carbon to the UVic ESCM. That is, the globally uniform density and extends only down to a depth of 3.35 m (Cmax). difference in the size of each carbon pool between simulations with and The UVic ESCM soil carbon component has been modified such that soil without permafrost carbon. All values are relative to the initial size of the o respiration does not occur in soil layers with a temperature below 0 C. frozen permafrost carbon pool, and a summation of all of the pools adds up to 100% for each year. Conclusions 1) The strength of the permafrost carbon feedback is nearly independent of emissions pathway followed.

2) Even if anthropogenic emissions are reduced to zero by 2013, CO Figure 4. Response of the carbon-cycle to a shutdown of industrial activity Acknowledgements 2 under varying climate sensitivities (to a doubling of CO ). Dotted lines may continue to build up in the atmosphere for the indefinite future as a 2 A.H. MacDougall is grateful for his support from NSERC CGS-D and his indicate a climate sensitivity of 2.0 oC, dashed lines a climate sensitivity of consequence of a self-sustaining positive feedback. supervision by A.J. Weaver 3.0 oC and solid lines a climate sensitivity of 4.5 oC. a.–f. Rate of change of oceanic and terrestrial carbon pools. The terrestrial and oceanic carbon 3) The permafrost carbon feedback leads to a previously unaccounted Figure 2. Emissions pathways diagnosed from representative concentration pools are displayed using opposite sign conventions to accommodate for warming of 0.25 (0.1 to 0.7)oC by 2100. comparison of the magnitude of the rates of change. The sudden increase in pathways 2.6, 4.5, 6.0 and 8.5. a. Anthropogenic carbon emissions rate. b. Cumulative anthropogenic carbon emissions. Note that DEP 2.6 requires the rate of carbon release from the terrestrial carbon pool when 4) Humanity may have already set in motion a positive climate negative carbon emissions. Historical emissions are from Boden et al. anthropogenic emissions are shutdown is a consequence the termination of References CO fertilization of land vegetation. g., h. Atmospheric CO concentrations in feedback that is beyond our ability to mitigate through simple (2011). 2 2 Schaefer, K., T. Zhang, L. Bruhwiler, and A.P. Barrett. (2011) Amount and timing of permafrost carbon release in response to climate response to shutdown of industrial activity. warming. Tellus 63B, 165–180. Boden, T., G. Marland, and B. Andres. (2011) Global CO2 emissions from fossil-fuel burning, cement manufacture, and gas flaring: 1751–2008. Carbon Dioxide Information Center. reductions in carbon emissions.

Andrew MacDougall PhD Candidate Climate Modelling Group School of Earth of Ocean Sciences, University of Victoria, BC, Canada

Summary of Research:

The University of Victoria Earth System Climate Model (UVic ESCM) is a climate model of intermediate complexity developed by the UVic Climate Modelling Group and associated groups over the previous 15 years. The UVic ESCM has a fully three dimensional ocean, dynamic vegetation component, oceanic and terrestrial carbon- cycle, dynamic sea-ice, permafrost, and glacier modules. The atmosphere is represented by a simplified energy and moisture balance module. The UVic ESCM is forced with external solar radiation and surface wind fields.

My PhD research aims to improve the UVic ESCM by: 1) incorporating permafrost carbon into the existing representation of the terrestrial carbon cycle; 2) making methane an independent radiatively active atmospheric gas; 3) improving the wetland scheme within the model (particularly for the tropics); 4) and introducing wetland methanogenesis into the UVic ESCM. &#$+'
'
"''$#
&$"
&#
&)
(+#&
+
($
&)$#
$
$#)#+$+'
%&"&$'(2
!
"'+&"#(
#
"$!
'"+!)$#





3
,#
+''(#
5 3,#3+''(#7,+3#!61
3

5/3"7,+3#!61
3
!!&
5#!3!!&7,+3#!61
3
3
&'#
5!3!!!"&'#7,+3#!61
3
+',
5&("3+',7,+3#!61
3
3
$!"#5#3$!"#7,+3#!63
!+'(&
$
&(
#
!"(1
& 
#,&'(/
"'(&"
(+/
&
0+
+,+#
,$-
$,
&)-
2$-#$(
-#
4-&4%
.)(&
+%7
%#
*0&$7
)+-#,-+(
$+$9
-
$,
,$-0-
2$-#$(

)(.(0)0,
*+'+),-
5)(7
2#$#
$,
)1+
DAA
'-+,
*9
#
)(.((-&
&$'-
)
-#$,
+"$)(
#,

'(
((0&
$+
-'*+-0+
)
;BE9D?7

0&4
'(
-'*+-0+
)
I9F?
(

(0+4
'(
)
;DE9G?9
#
'(
((0&
*+$*$-.)(
$,
CDC
''7
**+)3$'-&4
#&
&&,
,
+$(7
0+$("
-#
"+)2$("
,,)(7
2#$&,-
-#
+,-
&&,
,
,()29
()2'&-
)0+,
$(
&-
4:+&4
0(7
2#$&
0,
+%
"$(,
$(
&-
0(:+&4
0&49
+)2$("
,,)(
"(+&&4
(,
$(
&-
0"0,-9
#+
+
-2)
'$(
')+*#)&)"$&
-0+,

$(
-#$,
+8
-#
+$1+
))*&$(
(

+$1+
-++
2$-#
-4*$&
-0(+
1"-.)(9
#
))*&$(
&1,
+
)1+"+)2(
2$-#
&$3
+0,#7
2#$&
-#
%
,2'*,
+
)'$(-
2$-#
"+,,,
(
,",7
+("$("
+)'
&)2
"+,,
,*$
< 
 =
-
-#
&)2,-
,$-,
-)
-&&
"+,,,
(
,",
<
  
  =9
#
-0(+
)(-$(,

&+"+
$1+,$-4
)
1"-.)(
-4*,8

)((-+-
)(
-2)9
#
+,-7
+4
-0(+
)'$(-
2$-#
 

(
'),,
,*$,
<*)&4")(
+$',7
*&,,=9
#
,)(7
')$,-
-0(+
2$-#
+$)*#)+0'
-0,,)%,
(
2-
,$-,
)'$(-
2$-#
*#"(0'7
+3
(
+$)*#)+0'9


"&
"'+&"#(
(#
"''$#
"$!#

 Gas concentration of CO , CH , N O 2 4 2 CAA
#
9
!+.

BFA
 Chambers (opaque, transparent), photo- BAA
$"9

FA
-+
-&
*+)0
acoustic multi gas analyzer (INNOVA 1312) A
4&

9 4
')&

)'*+
;FA
2$-#
-

"4" 9  1-6 week(s) summer campaigns since 2004  ;BAA
;BFA

;CAA

 Sites distributed among tundra river terrace, ;CFA

;DAA
$"9

river floodplain and thaw ponds B
B
D
E
F
D
:
E
 E
03
*+)0
4
')&

)'*+
2$-#
-7
*#)-),4(-#,$,
')0&
)

#
 :
!+.



8
-
:
+4
-0(+
-+(,-
G

F

+("8
&))*&$(
-+(,-
E

D
#
*-&(;
$,

*+),,;,
')&
$"9




+(8 ;+$#
&$,-)(
*),$-,
2$-#
',,

C

03
*+)0
4
 :
"4"94&
,$"(
-)
,$'0&-

03,
+)'
*-
,)$&,9
-
E E ')&

)'*+
2$-#






2,.("
<)2(
,&)*
')1'(-=
B
)(,$,-
)
,$3
')0&,8
,)$&
*#4,$,7
C
-7
*#)-),4(-#,$,
A
')0&
)(
*+)0.)(7
 E
*+)0.)(7
)3$.)(
(


$+&,8
4
)1+$(
-)2+
&).)(,
;B
B
B
D
E
F
D
:
E
-+(,*)+-,7
)+"($
*+)0.)(7
*#)-),4(-#,$,7
(
2-+
-&9

-
! 
,$!+)$#
"$!#
/
$,&#
'/'("'
8
.
#
8
"$!
($-&


D
2$(
,*
#2
&%8



)($
-'*+-0+



;+$#
,$'(-+4
&)2&(
0(+&$(




 C
7 C
')&+
(,$.,






2$-#
*+'+),-


 E
2$-#
&,+
,*-+),)*4



)0+
)
'-#(
< ),
-),
 E
(&45+
(
)*(



&$'-
,(,$.1


*-#
$)+
HHAA=

($.&
*%
)
&%
+
)&&)2
4
+*$


)$&
-'*+-0+
*+) &,
CABA

Gill R3-50 SonicSo anemanemometer $+
-'*+-0+
(
*+$*$-.)(
)0-*0-
)
-#
&$'-
')&,
)+
+*$
&%
1&)*'(-
2$-#
#$"#
&%
DLT-100 $($..)(
+-
0-
0(+&$,.
')+*#)&)"4
LI7500 CCavityavity RingdownRingdown Laser  CCO2/H2O LLosos GatosGatos ResearchResearch scroll

$(
*+) &7
IRGA pump )(,
)&,$("
&%,
#2
&%
3*(,$)(


$+
-'*+-0+
(
#0'$$-4
CA'
$(
DA
4+,


*2+7
)2(2+

+$.)(


-
+$.)(
< J=
power supply: 

$(
data 180 kg 5 kW diesel generator logging wind generators 

-'9
*+,,0+
solar panels 3,,
*+$*$-.)(

),-
')&,
+,0&-
$(
+,
)
&%
+

!+
KHA
+.("
*)(,
.1
-#+'&
+),$)(
&)2
+;"+)2-#
$(-+)0,
,&;),$&&.)(
4+,
4
$(+,
&%
+$("
Greenhouse gas emission from Siberian arctic tundra due to degradation of continuous permafrost: field measurement and model simulation

J. van Huissteden, Y. Mi, A. Gallagher, L. B. Marchesini, A. Budischev, A. J. Dolman. Earth and Climate Cluster, Vrije University Amsterdam

Anthropogenic activities have dramatically increased the concentration of both carbon dioxide (CO2) and methane (CH4) within the Earths atmosphere. There is growing concern that continued global warming and climate change may result in the degradation of permafrost in the Siberian Arctic. This loss of permafrost and the resulting increase in the active layer depth may release old carbon, previously locked within the permafrost, while also creating anoxic conditions ideal for methane producing bacteria, creating a positive feed back further accelerating the degradation of Siberian permafrost. Permafrost degradation, will be experienced as both large and small scale phenomena, such as the expansion of thaw lakes, and small-scale features, such as superficial pond formation and mass wasting.

We are investigating the effects of both small and large scale permafrost degradation features on the carbon and greenhouse gas balance of the Siberian tundra. Our research site is located within the Kytalyk National Park, Sakha Republic, Northeastern Siberia. It is situated within a continuous permafrost zone. Since 2003 we have measured greenhouse gas, using both the chamber gas monitoring system (Innova 1312) and Eddy covariance systems (LI7500, LI7700, Los Gatos). Additionally, we record meteorological data as well as soil physical properties. The wetland carbon balance model Peatland-VU and thaw lake evolution model, Thawlake, are designed to simulation methane emission from peat soil and thaw lake dynamic process respectively. We test both models against the field data, which agree well with the field measurements.

Andrew D. Parsekian Department of Geophysics Stanford University

I am interested in a variety of science questions related to innovative applications of near-surface geophysics to permafrost environments. My goal is to address data gaps using geophysical methods where traditional measurements have been unable to fully resolve information about the target or process being studied. Geophysical measurements have the advantage of being spatially extensive, portable, non- invasive and often deployable in time-lapse mode. In the past I have used ground penetrating radar (GPR) to study floating vegetation on the margins of thermokarst lakes on the Seward Peninsula in Alaska. Currently I am investigating the suitability of surface-based nuclear magnetic resonance (surface NMR) to study thermokarst lakes. Thermokarst lakes are interesting targets because they have a high relevance to carbon cycling and because it is particularly challenging to make direct measurements of thaw bulb properties. The surface NMR method is unique among non-invasive geophysical measurements because of its direct sensitivity to liquid water content. Therefore, permafrost thaw features such as thawed sediments below lakes are appropriate geophysical targets.

Rob Striegl Research Hydrologist/Biogeochemist U.S. Geological Survey, National Research Program Boulder, Colorado USA [email protected]

Rob’s research focuses on the generation, chemical characterization, biogeochemical transformation and transport of inorganic and organic carbon by aquatic ecosystems and on carbon dioxide and methane exchange across the water-air interface. He is the lead aquatic scientist of USGS studies of the Yukon River and its tributaries and his group’s current research is centered on greenhouse gas exchange with the inland waters of interior Alaska and on permafrost thaw effects on the hydrology and C biogeochemistry of subarctic regions. Rob is also active with quantifying riverine C exports to the Arctic Ocean as part of the NSF AON Arctic Great Rivers Observatory Project. The effect of permafrost carbon on Arctic aquatic ecosystems Riverine flux of A Suzanne Tank Dept. of Geography, York University, Toronto, Canada dissolved carbon across circumpolar Permafrost carbon in lakes of the Mackenzie Delta, western Canadian permafrost gradients Suzanne E. Tank1, Lance F.W. Lesack2, Ray H. Hesslein3, Christopher L. Arctic | Suzanne E. Tank1, Karen E. 4 2,5 1 2 Osburn , and Jolie A.L. Garies | Department of Geography, York University, Department of Frey2, Robert G. Striegl3, Geography, Simon Fraser University, 3Freshwater Institute, Fisheries and Oceans Canada, 4Department of Marine, 4 Earth, & Atmospheric Sciences, North Carolina State University, 5Aurora Research Institute, Inuvik NT Peter A. Raymond , Robert M. Holmes5, James W. Lakes of the Mackenzie Delta exist 6 across a clear elevational gradient, with McClelland , and Bruce J. low elevation lakes continually Peterson7 connected to inputs of riverwater from 1 2 3 4 the larger Mackenzie system, and higher Department of Geography, York University, Department of Geography, Clark University, USGS, Yale School 5 6 elevation lakes often subjected to of Forestry and Environmental Sciences, Woods Hole Research Center, Marine Science Institute, University 7 significant thermokarst along their of Texas at Austin, Marine Biological Laboratory While much of the dissolved organic margins. High elevation lakes that are carbon (DOC) within rivers is not thermokarst-affected have high B C destined for mineralization to CO , a levels of macrophyte productivity, as a 2 result of their shallow depth and periodic receipt of nutrient pulses during the substantial fraction of riverine - yearly spring flood. This gradient in lake type enables an investigation of the effects bicarbonate (HCO3 ) represents a CO2 of thermokarst C on within-lake biogeochemistry. sink, as a result of weathering processes that sequester CO2 as - HCO3 . We explored landscape-level - A Lakes experiencing thermokarst along their controls on DOC and HCO3 flux in a margins are strong CO2 emitters when series of pan-Arctic sub-catchments, compared to their proximate, non- with a specific focus on the effect of thermokarst counterparts (A). While bacteria permafrost on dissolved C flux. To in non-thermokarst lakes preferentially , Clark do this, we undertook a multivariate consume C derived from within-lake primary analysis that partitioned the variance Fig3FINAL_circumborealproduction, thermokarst bacteria display a attributable to known, key regulators strong signal of terrestrial C consumption (B). of dissolved C flux (runoff, lithology, peat) prior to examining the effect of permafrost, using riverine biogeochemistry data from 236 sub- B catchments scattered throughout the circum-Arctic (A).

Across these diverse catchments, - controls on HCO3 flux were near- universal, and decreasing permafrost extent was consistently associated - with increases in HCO3 (B, bottom panel). In contrast, permafrost had contrasting and region-specific effects on DOC flux, even after the However, Mackenzie Delta bacteria variation attributable to other key C consuming thermokarst C (TK) have drivers had been accounted for (C). lower biomass production rates than The calculated potential range of CO2 bacteria consuming other local C sources sequestered via weathering across (floodwater, non-flood riverwater, and these watersheds indicates that macrophytic C). Consumption of decreasing permafrost extent is thermokarst C also results in a higher associated with increases in proportion of consumed C being weathering-mediated CO2 fixation apportioned to respiration, versus that across broad spatial scales, an effect which is incorporated as biomass that could counterbalance some of production (i.e., lower growth the organic C mineralization that is efficiencies; panel C). predicted with declining permafrost. Suzanne Tank Assistant Professor York University, Toronto, Canada

Research Overview

My research program focuses on understanding the implications of biogeochemical fluxes occurring across the land-freshwater-ocean continuum. Within this framework, my ongoing research activities are focused on dynamics within the streams, rivers, and lakes of the western Canadian Arctic, and the biogeochemistry of large rivers throughout the circumpolar. The research encompassed within my overall program occurs across a variety of spatial and temporal scales, and incorporates experimental, survey-based, and modeling approaches. Several of my ongoing research foci include:

x Understanding the fate of terrigenous DOC inputs within aquatic ecosystems, and the effects of DOC subsidies on aquatic food web structure and aquatic ecosystem function x Using stream and river biogeochemistry to explore how changes in permafrost extent and other

landscape-level drivers affect weathering processes on land, and weathering-mediated CO2 consumption x Using chemical signatures to explore the source of various biogeochemical constituents within large Arctic rivers, and the significance of their flux to the Arctic Ocean nearshore

x Quantifying and exploring the controls on the flux of gasses (CO2, CH4) across the air-water interface in Arctic aquatic ecosystems x Exploring the role of ecological stressors in shaping aquatic ecosystem function

Recent, relevant publications include:

Tank, S.E., K.E. Frey, R.G. Striegl, P.A. Raymond, R.M. Holmes, J.W. McClelland, and B.J. Peterson. 2012. Landscape-level controls on dissolved carbon flux from diverse catchments of the circumboreal. Global Biogeochemical Cycles doi:10.1029/2012GB004299. Tank, S.E., L.F.W. Lesack, J.A.L Gareis, C.L. Osburn and R.H. Hesslein. 2011. Multiple tracers demonstrate distinct sources of dissolved organic matter to lakes of the Mackenzie Delta, western Canadian Arctic. Limnology and Oceanography 56: 1297-1309. doi: 10.4319/lo.2011.56.4.1297.

Tank, S.E., L.F.W. Lesack and R.H. Hesslein. 2009. Northern delta lakes as summertime CO2 absorbers within the arctic landscape. Ecosystems 12: 144-157.

Claire Treat University of New Hampshire Permafrost RCN 26 November 2012

Abstract:

High latitudes are experiencing effects of climate change such as soil warming, thawing permafrost, altered hydrology, and longer growing seasons due to warmer temperatures. An estimated 50% of the global belowground soil carbon pool (SOC) is stored in high latitudes. Therefore, the release of soil C stored in permafrost soils could provide a significant positive feedback to climatic change. However, the controls on soil carbon release are poorly understood and may differ between mineral and organic soils. Using an experimental approach, I quantified carbon storage and release in soil cores from northern permafrost peatlands and determined whether the controls are due to inherent differences in soils or whether the controls are purely due to soil climate.

In order to measure the effect of soil climate on carbon dynamics, I conducted an incubation experiment using replicate peat cores from two boreal and tundra peatland sites with intact permafrost to represent Alaskan peatlands. I applied a range of temperature and moisture treatments during the experiment and measured emissions of CO2 and CH4. I also characterized total microbial biomass, dissolved organic carbon, and peat chemistry. Preliminary results indicate significant differences in CO2 flux between ecosystems and depths (active layer vs. permafrost) that are likely due to autochthonous differences in peat chemistry and microbial biomass. These differences indicate that the response of soil C to climate change may vary by ecosystem type and may be larger in tundra peatlands. The differential response of C emissions by ecosystem type indicates that tundra peatlands will be a positive feedback to climate change; the forcing magnitude depends on their spatial extent globally, a factor that is still unknown.

UsinganAcousticSystemtoEstimatetheTimingandMagnitudeofEbullitionReleasefromWetlandEcosystems RuthK.Varner1,MichaelW.Palace1,JillianM.Lennartz1,PatrickM.Crill2,MartinWik2,JacquelineAmante1,ChristopherDorich1, JenniferW.Harden3,StephanieA.Ewing4,MerrittR.Turetsky5 1InstitutefortheStudyofEarth,OceansandSpace,UniversityofNewHampshire,Durham,NH;2DepartmentofGeologicalSciences,StockholmUniversity,Stockholm, Sweden;3USGeologicalSurvey,MenloPark,CA;4DepartmentofLandResources&EnvironmentalScience,MontanaStateUniversity,Bozeman,MT;5Departmentof IntegrativeBiology,UniversityofGuelph,Guelph,ON,CA.

Introduction ResultsfromFieldDeployment

Understandingthemagnitudeandfrequencyofmethane(CH4)releasethroughebullition(bubbling)inwatersaturated Aftersuccessfullaboratoryandlocalfieldtesting,ourinstrumentsweredeployedinsummer2011atatemperatefen(Sallie’sFen,NH,USA),asubarcticpeatandlake(Stordalen,Abisko,Sweden)andtwo ecosystemssuchasbogs,fensandlakesisimportanttoboththeatmosphericandecosystemssciencecommunity.Thecontrolson locationsinsubarcticAlaska(APEXResearchSite,Fairbanks,AKandInnoko NationalWildlifeRefuge). episodicbubblereleasesmustbeidentifiedinordertounderstandtheresponseoftheseecosystemstofutureclimateforcing.We havedevelopedandfieldtestedaninexpensivearrayofsampling/monitoringinstrumentstoidentifythefrequencyand magnitudeofbubblingeventswhichallowsustocorrelatebubbledatawithpotentialdriverssuchaschangesinatmospheric pressure,windspeed,andtemperature.

Fig1a Figure1a. 2400 Schematicof Figure1b.Laboratorycalibrationof 2200 Instrument 1 prototype threeseparateinstrumentsand Instrument 2 instrument.A. 2000 Minnaert relationship(lightgreen). Instrument 3 ThreeinchPVC Minnaert 5A. 5B. 1800 pipe.B.Lower Fig4. funnel.C. 1600 Hydrophone.D. Figure4.Mapofacousticsensorfieldinstallationsfor2011. Upperfunnel.E. 1400

Manualsamppgling uency (Hz) qq tubewith 1200 Figure5A.Sallie’sFentemperatepeatland,Barrington,NH,USA.B. Stordalen mirefensite,Abisko, Fre stopcock.F.PVC 1000 Sweden,C.Innoko Flatsnewbogsite,Innoko WildlifeRefuge,AK,D.Lakesensorandrecorderboxat elbowto Stordalen,Abisko,Sweden,E.APEXResearchsite,Fairbanks,AK. 5C. 5D. 5E. eliminate 800 rainwaterand 600 Fig1b somenoise 7/11 7/18 7/25 8/1 8/8 8/15 8/22 Figure6a.Air(darkred)and Sensor AB2_L - Stordalen Mire Peat 25 25 interference. 400 Temperature,

C 5 cm soil 5cm(darkgray)and100cm o 30 30 0.0 0.1 0.2 0.3 0.4 20 Air 6a. 20 100cm soil (lightgray)soiltemperatures Bubble Volume (ml) 15 15 Acoustic Sensor fromtheANS Manual sampling 25 25

10 10 C

meteorologicalstation,Fig l u mperature, o mulative volume loss, ml ml loss, volume mulative C mm The prototypeacoustic  ebullition sensor consists of a nested, inverted funnel design with a hydrophone for detecting bubbles ee 5 5 T 6b.Atmosph eri cpressure risingthroughthepeatthatthenhitthemicrophone(Figure1a).Thedesignalsooffersawaytosamplethegasescollectedfrom measuredattheANS 20 20 30 980 thefunnelstodeterminetheconcentrationofCH4.Laboratorycalibrationoftheinstrumentresultedinanequationthatrelates meteorologicalstation Bubbles per day frequencyofbubbleshittingthemicrophonewithbubblevolume(Figure1b).Theoretically,bubblesizecanbecalculatedusing Air pressure 6b. (blackline)anddailytotal 15 15 25 theMinnaert Resonanceequationforthefrequencyofabubblesuspendedinawatercolumn(Minnaert ,1933). Mean of four sensors A 970 pressure,tmospheric ebullitioneventsfrom4 1/2 f =1/2ʋr(3ɶPA/ʌl) peatsensors(bluetriangles) 10 10 where:f=resonancefrequencyofabubblesuspendedinwater(Hz);r=theradiusofthesphericalbubble(meters);ԃ =the 20 intheStordalen Mirepeat. Cumulative volume loss, polytrophiccoefficient(1.0assumingisothermalconditions,1.4assumingadiabatic);PA =theambientpressure(Pascals);ʌl = 960 5 5 densityofwater(kgmͲ3). 15 7 Figure7. Cumulative 0 0 Bubbles per day 10 950 m 6/27 7/4 7/11 7/18 7/25 8/1 8/8 8/15

Sppgpectrogramsproduceddurin gcalibration b Fig2a Fig2b Fig3 ebullitive loss from the revealthesignatureofabubblewhenit Stordalen Mirepeatsite 2011 impactsthehydrophone(Fig2aͲd). 5 940 measuredmanually(black Differentvolumesgreatdifferent circlesandline)and PreliminaryEstimates frequencyresponses.Imagesshownhere 0 7/11 7/18 7/25 8/1 8/8 8/15 8/22 acoustically(darkredline). arefromSoundForgesoftwarebySony. 2011 Site ChamberFlux EbullitiveFlux Stordalen peat 130±75* 31±27 Inthefield,audiodatawasrecorded 120 120 250 Peat Sensors 250 Peat Sensors - Sallie's Fen Manual fluxes (fen) continuouslyusingadigitalaudio Peat Sensors - Stordalen recorder(ZoonH4ndigitalrecorder) 100 Lake Sensors - Inre Harrsjön 100 Stordalen Lake 7± 10*10±10

200 200 Manua attachedtotwoebullitionsensors(Fig3). m C -1

Fig2d x E

80 80 g l Chamber Fluxes l Chamber d Fig2c -1 H uu xx CH bullition Flux Sallie’ ssFenpeatFenpeat 128± 77 56± 42 d -2 4 150 150 , mg m -2

Audiowasrecordedasanmp3compressedaudiofileatasamplerateof160kbits/sec. 4 m 60 60 -2

Usingthisformatandstereoinput,allowingfortwosensorstoberecordedwitheach , mg m d 4 -2

, mg , mg m *Stordalen peatfromBäckstrand etal.,2010;Stordalen Lakefluxesfromfloatingchambers(JoUhlbäck,unpublisheddata.) -1 4 d 100 100 Ebullition Fl Ebullition CH device,wewereabletorecordcontinuouslyfor20days.Audiowasconvertedto -1 Ebullition Flu Ebullition uncompressedaudiofilesforspeedincomputation.Audiodatawasprocessedusing CH 40 40 Acknowledgements FundingforthisworkhasbeenprovidedthroughgrantsfromtheUSGSandaFaculty MATLAB,searchingin0.5secondincrementalsectionsforspecificfundamental 50 50 20 20 DevelopmentgrantthroughtheNHSpaceGrantConsortium.Wewouldalsoliketothank frequenciesthatarerelatedtoourcalibratedaudioevents.Time,fundamental themanystudentsandstaffwhoassistedintheinstallationandcollectionofsamplesand soundfilesateachsite. frequency,andestimatedbubblesize(usinglaboratorycalibrationcurves)wereoutput 0 0 0 0 5/1 6/1 7/1 8/1 9/1 10/1 11/1 toatextfileforanalysis.Inaddition,eacheventwascutoutofthelongeraudiofile 6/1 7/1 8/1 9/1 10/1 11/1 12/1 References Ͳ2 Bäckstrand,K.,P. M.Crill,M.JackowiczͲKorczynski,M.Mastepanov,T.R.Christensen,andD.Bastviken andplacedinadirectorywithnumberofebullitionevent,sensornumber,andtime, Figure8.Manuallymeasuredebullitive fluxofCH4 (mgCH4 m Figure9.Chamberandmanuallymeasuredebullitive flux (2010),Annualcarbongasbudgetforasubarcticpeatland,NorthernSweden,Biogeosciences,7,95Ͳ108. Ͳ1 Ͳ2 Ͳ1 Minnaert,M.,(1933),OnmusicalairͲbubblesandthesoundofrunningwater.Philos.Mag.,16.p235–248. allowingformanualinterpretationoftheebullitionevent. d )forSallie’sFenandStordalen sites. ofCH4 (mgCH4 m d )forSallie’sFenandStordalen sites Ruth K. Varner, Research Associate Professor Earth Systems Research Center, Institute for the Study of Earth, Oceans and Space and Department of Earth Sciences University of New Hampshire, Durham, NH

My research has focused on understanding the magnitude of and controls on the exchange of radiatively important trace gases to the atmosphere from aquatic and terrestrial (natural and anthropogenic) environments. The trace gases that are the focus of my research can be lumped into halogenated compounds (methyl halides, CH2ClI), carbon (CO2, CH4) and nitrogen (N2O, NO) species. Having worked in remote and unique locations I have addressed and met the associated challenges in sampling and in maintaining year-round measurements in such environments. My work has been multi- faceted: measurement design and implementation and data collection, analysis and interpretation. Collaborative work with undergraduate and graduate students, university colleagues, and industry partners has also been a critical part of my research program. Currently, I am focused on studying the emission of methane from a variety of wetland ecosystems. Specifically I am using acoustic sensors to measure ebullition in peatlands and lakes. I have deployed these sensors in Sweden, Ontario, and the US (AK, ME and NH). I am also the director of the Northern Ecosystems Research for Undergraduates, an NSF REU program that brings students to UNH and the Abisko Scientific Research Station to study the impact of climate change on permafrost ecosystems. This work includes studying CO2 and CH4 fluxes, controls on lake emission of CH4, cycling of Hg in these ecosystems, and ebullition fluxes from lakes, ponds and peat. Most recently, I am leading an effort to investigate northern peatland CH4 dynamics by synthesizing measurements, remote sensing and modeling from local to regional to continental scales. This project will establish new measurements and leverage existing datasets of methane emissions from five well-studied research sites that span four northern climatic zones. Using a unique scaling approach, the emissions from northern wetlands will be determined so that a global analysis can be performed. This project will both implement newly emerging measurement technology (isotopologues of CH4) and allow for more accurate representation of methane emissions on a global scale.

1

High biolability of ancient permafrost carbon upon thaw

Jorien E. Vonk1*, Paul J. Mann2, Sergey Davydov3, Anna Davydova3, Robert G. M. Spencer2, William V. Sobczak4, John Schade5, Nikita Zimov3, Sergei Zimov3, 2 1 2 Ekaterina B. Bulygina , Timothy E. Eglinton , Robert M. Holmes Background picture © Chris Linder (www.chrislinder.com)

1Geological Institute, Swiss Federal Institute of Technology (ETH), Zürich, Switzerland, 2Woods Hole Research Center, Woods Hole, MA, U.S.A., 3Pacific Institute for Geography, Far-Eastern Branch, Russian Academy of Sciences, North-East Science Station, Cherskiy, Republic of Sakha (Yakutia), Russian Federation, 4Holy Cross College, Worcester, MA, U.S.A., 5St. Olaf College, Northfield, MN, U.S.A. * Corresponding author: [email protected]

Half of the global stock of soil organic carbon (OC) is stored in Arctic permafrost [1]. About one third of this pool consists of so-called Yedoma, organic-rich deposits that were formed during the Pleistocene [2]. Previous studies show rapid respiration of Yedoma upon thawing, with the potential release of large quantities of relict OC into the contemporary C cycle [3,4]. The fluvial and coastal reactivity of this OC, however, and its fate remain unclear. Duvannyi Yar is a well-studied Yedoma exposure on the banks of Kolyma River in Northeastern Siberia. It can serve as a model for the >7000 km long East Siberian Arctic coastline that is dominated by similarly exposed Yedoma cliffs, and is increasingly vulnerable to erosion with climate warming-induced decreases in sea-ice, and increases in storms and wave-fetch [5]. Our study site is located in NE Siberia (above) with the North- East Science Station in Cherskiy as our base. Satellite image (www.visibleearth.nasa.gov) taken on 24 August 2000 showing significant erosion of the East Grey areas (right) depict the estimated extent of Yedoma [6]. These deposits Siberian Yedoma coast. Our study site Duvannyi Yar (estimated pool size ca. 400 Pg) were formed during glacial times and are consists of similar Yedoma deposits. characterized by their high organic carbon content (2-5%) and the abundance of ice wedges (20-50%) [2,7]. We performed experiments with thawing Yedoma at the Duvannyi Yar exposures, located ca. 120 km upriver from Cherskiy. Duvannyi Yar exposures on Kolyma river bank

Abundant ice wedges

Permafrost thaw causes the slopes of Duvannyi Yar to retreat with ca. 3-5 m/y. The ice wedge thaw produces mud streams that drain into the Kolyma River. These streams are heavily loaded with freshly thawed Yedoma sediments (particulate OC ca. 7-10 g/L; dissolved OC 150-300 mg/L). The OC is very old: particulate OC is 19,000-38,000 14C-yrs, dissolved OC 16,000-30,000 14C-yrs.

Mud streams form small deltas We performed experiments with filtered and unfiltered Yedoma stream water. With a similar set of Duvannyi Yar spiked solutions (0.5, 1.0 and 10% Degradation rate constants calculated over 14 days were in Different amounts of Yedoma (“DY”) material were added to Kolyma River and Duvanniy Yar “DY” additions) we set up dark incubations of filtered the range of 0.41 for Yedoma-DOC, 0.19 for Kolyma River These streams carry ca. 650 gram Arctic Ocean water. Losses in O (mg/L) were measured over time, serving as an water (0.7μm). We measured dissolved organic carbon (DOC) and East Siberian Sea DOC, and 0.19 to 0.37 for Yedoma- mud per liter 2 indicator for carbon consumption. concentrations at T=0, T=14 and T=28d. The vials were shaken and DOC dilutions (Fig. 3b). kept oxygenated during the experiment. • O2 losses in DY spiked river and ocean water were larger for unfiltered than filtered samples. • The 0.5 and 1.0% additions showed a slightly higher DOC loss • 10% DY spiked water showed strongest O2 (17-20% after 14d) than un-spiked waters (17%). losses over time, with anoxia occurring after ca. • The 10% additions increased the DOC loss: 31-33% after 14d. 32-40 hours in the 10% solutions. • Filtered DY water showed a DOC loss of 34%( 14d).


Extracellular enzyme activities 
measured on BOD waters at day 
• Yedoma OC, hosting ca. 25% of the total belowground soil carbon, is very old, yet highly 
0 and day 10. Phos activity is 

typically increased in response biologically reactive upon mobilization - both in fluvial and coastal settings. to P limitation, Leu increases in 
• Our experiments show that Yedoma OC of >21,000 yrs old, lost 34% of its initial DOC after 14d response to N limitation, and CONCLUSIONS: 
PhOx increases with molecular incubation in the dark at ambient river temperature.
aromaticity. See also poster

  
  
 
 
#9444 in this session. • P and N seem to limit the continued degradation of old organic C






We would like to acknowledge financial support from the US-NSF (POLARIS project # 1044610), the Dutch-NWO (Rubicon # 825.10.022), and the Russian Foundation for Basic Research (RFBR). Kolyma 1% DY + Kolyma 10% DY + Kolyma [1] Tarnocai et al. Global Biogeochem. Cycles 23, GB2023 (2009). [2] Zimov et al. Science 312, 1612-1613 (2006). [3] Rivkina et al. Geomicrobiol. J. 15, 187-193 (1998). [4] Dutta et al. Glob. Change Biol. 12, 2336-2351 (2006). [5] Intergovernmental Panel on Climate Change (IPCC), The Scientific Basis (Cambridge Univ. Press, New York, 2007). [6] Romanovskii, N. N. Fundamentals of the Cryogenesis of the Lithosphere (University Press, Moscow, 336pp.) (1993). [7] Lantuit et al. Estuaries and Coasts DOI 10.1007/s12237-010-9362-6 (2011)  

 

+) +
% -)* +0

 
'+6
&
)+
 %*
% -)* +0
&
)&% %%
,'*+#%
@
)+ 
%+)
?AD@

+) +4
 
+ )#%*
66&/
C=B
F?=
?<
>A?
A>?>
EC<<

)&% %%4
 
+ )#%*
66&%";,,6%#



 


5
 72)  4
&#& #
%*+ +,+4

B?4
&%%*+)**
A4
D
2)  4
. +1)#%4
F@=
@@B?>
@BEE4
!&) %6-&%";).6+ 16

0
)*)
&,**
&%
 && $ #
0# %
%
)+ 
) &%*4
. +
$' * *
&%
+ 
+
&
+ . %
')$)&*+
)&%
%
#,- #
%
&*+#
%- )&%$%+*6

$
#*&
%+)*+
%
+ 
$'+*
&
# $+
.)$ %
&%
+))*+) #
%
*,*
')$)&*+4
)+ 
0)&#&04
* $%++ &%
')&***4
%
&*+#
)&* &%
0%$ *6


,*

)%
&
+ % (,*
+&
*+,0
+ *
+&' *4
*,
*
$&#,#)
%
*&+&' 
& $ *+)04
) &% 
*&+&'*4
$ %)#&08) %
* 1
%#0**4
9 #7:
%,+ &%
*+, *4
/+)##,#)
%10$
+ - +0
%#0**4
%
&'+ #
+ % (,*6
,) %
$0
 
9+&" &#$
% -)* +04
.%:

*+, 
+ 
)#*
%
')&** %
&
+))*+) #
$++)
%
9*,7:)+ 
&*+#
*0*+$*4
. +

&,*
&%
+ 
%&)+ )%
#+ 

%
'+-
%
*+
 ) %
* #
**6
,) %
$0
'&*+7&
+
 72) 
9. +1)#%:

&%%+)+
$&)
&%
)+ 
) -)*
*0*+$*
%
#+*
9 �$
 -)4
"%1 
#+:6
)&$
%,)0
><=?

. ##

*
+
+) +
% -)* +04
 
+ )#%*4
. )

. ##
&$ %
+ 
#,- #
%
$) %8&*+#
 #*
+&
*+,0
)&%
0# %
%
+ 
)+ 6


Satellite Microwave Detection of Contrasting Changes in Surface Inundation Across Pan-Arctic Permafrost Zones Jennifer D. Watts1,2,, John S. Kimball1,2, Lucas A. Jones1,2, Ronny Schroeder3,4, Kyle C. McDonald3,4 1Flathead Lake Biological Station, The University of Montana 32125 Biostation Lane, Polson, MT, 59860-9659 2Numerical Terradynamic Simulation Group, The University of Montana, Missoula, MT, 59812 3Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, CA, 91109 4Department of Earth and Atmospheric Sciences, The City College of New York, City University of New York, New York, NY 10031 B21D-0397 Contact: [email protected] Websites: www.ntsg.umt.edu/project/amsrelp & www.freezethaw.ntsg.umt.edu/visualization.htm

Introduction Fw Spatial and Seasonal Variability Surface water inundation strongly influences land-atmosphere water, energy and carbon (CO2, CH4) exchange in high Latitudinal Characteristics Regional Patterns latitude systems. Northern regions are particularly vulnerable to changes in surface water fraction (Fw). Permafrost thaw can initially increase Fw, whereas surface drainage occurs with continued degradation. Optical satellite remote sensing has been used to detect localized, fine-scale changes in Arctic water bodies. However, optical retrievals are susceptible to cloud and aerosol contamination, and often have limited repeat coverage. Passive microwave retrievals are well-suited to monitor Arctic regions given daily overpass at northern latitudes, strong sensitivity to surface water conditions, and insensitivity to atmosphere contamination and solar illumination effects. We examine recent (2003- 2010) Fw patterns across pan-Arctic (> 50°N) permafrost zones using daily satellite passive microwave retrievals from the Advanced Microwave Scanning Radiometer for EOS (AMSR-E). The AMSR-E Fw retrievals were verified using finer-scale static open water maps (Landsat; MOD44W) and discharge records (Q; m3 s-1) from the major Arctic river

basins. Monthly AMSR-E Fw mean (Fwavg) and maximum (Fwmax) values were used to evaluate seasonal and interannual change in surface inundation across pan-Arctic permafrost regions.

Seasonal melt and precipitation patterns vary by latitude. This is reflected in AMSR-E Fw inundation AMSR-E Fw Verification extent for the 2010 summer period (above). Non-frozen surface water area is highest in June in lower latitude regions (50-60°N), whereas landscapes further north (>60°N) peak mid to late July. Fw decline ƒ Daily AMSR-E Fw retrievals: 2003-2010, 25 km2 resolution EASE-Grid, 18.7 & 23.8 GHz, in late summer at lower latitudes is influenced by seasonal increases in soil water infiltration and water AMSR-E Fw inundation extent is highest in wetland complexes within the major Arctic AM overpass1; http://freezethaw.ntsg.umt.edu/dataholdings.htm loss from river transport and evapotranspiration. river basins, which include the Canadian Shield, Yukon River Delta, Lena, Ob-Yenisey, and ƒ UMD Global 250-m Land Water Mask (MOD44W; MODIS (>60°N) & Shuttle Radar Topography (<60°N)) Volga lowlands (above). Regions outlined in red denote watersheds where river Q was ƒ Landsat 30-m maps for north-east Europe, Alaska, and north-central Canada used for Fw verification. ƒ Monthly Q (m3/s) for Yukon, Mackenzie, Ob, Yenisei, & Lena basins; http://rims.unh.edu Fw Changes in Permafrost Regions

A per-grid analysis of annual AMSR-E Fwavg from 2003-2010 shows widespread inundation increase Although annual Fw extent is relatively consistent from 2003-2010, between-year (below) within the continuous permafrost region (in blue; 92% of grid cells with significant trend show variability in seasonal Fw patterns is evident, particularly in Eurasia (below). Early Arctic Basin Discharge wetting; p < 0.1). This is also observed in discontinuous permafrost, but to a lesser extent (82%). Regions season melt or precipitation on frozen soil can lead to short-term increases in Fw (e.g. of widespread drying (71%; in red) occur within the more degraded sporadic and isolated permafrost. spring 2008). Warm periods with minimal rainfall contribute to a sharp decline in Areas of Fw decrease in Eastern Canada appear to coincide with regional warming trends. These inundation extent following peak summer Fw. This is especially apparent in Fw (in patterns of contrasting inundation are consistent with reports from previous, regional studies and max black) which is more representative of short-term flooding events than Fwavg (in grey). Basin-averaged AMSR-E Fwavg corresponds well with expectations of Fw change with widespread permafrost degradation. summer Q (left) (R > 0.71). Positive Fw and Q anomalies in 2007 and 2009 coincide with documented wet years. Summer drought is reflected in the Mackenzie and Yenisey Fw and Q records for the 2004 period.

The AMSR-E Fwavg composite (2003-2010) compares well (0.71 < R2 < 0.84) with alternative static open water maps derived from finer scale (30-m and 250-m resolution) Landsat and MOD44W datasets (below). AMSR-E Fw is

generally lower than static Fw (Fws; in red) in areas of strong seasonal or interannual variability in inundation

extent. Whereas AMSR-E Fw is higher than Fws in wetland-dominated regions (in blue) due to differences in temporal retrieval period and greater microwave sensitivity to surface water coverage in vegetated landscapes compared to classifications of optical imagery. Study Conclusions • The 2003-2010 AMSR-E record indicates large seasonal and interannual variability in pan- Arctic surface water inundation, which likely correspond to regional fluctuations in temperature Static Open Water Maps and precipitation. Conservative results from a forward model sensitivity analysis indicate that total Fw retrieval accuracy is within + 4.1% (RMSE) due to strong microwave sensitivity to open water variability. 2 Changes in annual Fw area (km ) are observed only in the Fwmax records when regions are • This analysis shows widespread wetting across continuous permafrost regions and a decline considered as a whole, instead of on a per-grid basis (below). These changes are primarily driven in open water area in sporadic and isolated permafrost zones, particularly in Eastern Canada. by increasing Fwmax within the continuous permafrost zone, which could indicate an increase in These patterns are consistent with observations from regional studies. However, overall annual short-term flooding events. Significant increase in the annual number of grid cells with open inundation area in the pan-Arctic (>50°N) remained relatively stable during the 2003-2010 water inundation is observed only when constrained to permafrost regions. An increase in Fw period. -1 duration by 0.76 days yr (2003-2010) for regions >50°N period may reflect a lengthening non- • Regional changes observed in the AMSR-E Fw record compliment finer-scale permafrost frozen period. monitoring efforts and documented variability in surface inundation extent may help constrain * p< 0.10 ** p < 0.05 pan-Arctic lake and wetland CO2, CH4 emission estimates Fw Fw Fw Area Region Fw Fw Count Duration avg max References & Acknowledgement

pan-Arctic (> 50°N) 0.34 0.71** 0.33 0.24 This work was supported under the Jet Propulsion Laboratory, California Institute of Technology under contract to the National North America 0.24 0.71** 0.14 0.33 Aeronautics and Space Administration, NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Eurasia 0.33 0.52 -0.05 0.14 program.

All Permafrost Zones 0.81** 0.71** 0.43 0.62* Jones, L.A., and J.S. Kimball. 2011. Daily global land surface parameters derived from AMSR-E. Boulder Colorado USA: National Continuous 0.71** 0.90** 0.53 0.71** Snow and Ice Data Center. Digital media. http://nsidc.org/data/nsidc-0451.html.

Discontinuous 0.62* 0.71** 0.24 0.43 Watts, J.D., J.S. Kimball, L.A. Jones, R. Schroeder, and K.C. McDonald. 2012. Satellite microwave remote sensing of contrasting Sporadic & Isolated 0.62* 0.52 -0.14 0.42 surface water inundation changes within the Arctic-Boreal region. Remote Sensing of Environment 127: 223-236.

Dr. John S. Kimball Numerical Terradynamic Simulation Group (NTSG) The University of Montana, Missoula Flathead Lake Biological Station. Polson, MT

The University of Montana’s Numerical Terradynamic Simulation Group (NTSG) is developing new approaches for landscape ecological and hydrological analyses. Our major focus is to understand how terrestrial ecosystems respond to climate variability and influence energy, water and carbon cycles. We conduct research over a broad range of spatial scales, from individual landscape units to basin, continental and global domains. NTSG has strong emphasis in the application of ecological theory and environmental analysis using satellite remote sensing and computational process modeling. NTSG is a NASA Earth Science Information Partner (ESIP) and is involved with the NASA Earth Observing System (EOS) as a repository for a variety of global land data products. Recent NTSG research activities include: developing a long-term global data record for land surface freeze-thaw dynamics from satellite microwave remote sensing for quantifying changing frozen season constraints on land surface phenology and ecosystem processes; developing a global land parameter record for ecosystem studies from the AMSR-E passive microwave remote sensing record. We’ve applied these new data records to document and understand recent climate change impacts to northern boreal-Arctic ecosystems, including changing surface water inundation and permafrost conditions, vegetation growing seasons and associated impacts to land-atmosphere carbon (CO2 and CH4) exchange and evapotranspiration. We are currently developing a Level 4 terrestrial carbon product for the NASA Soil Moisture Active Passive (SMAP) mission for operational estimation and monitoring of global terrestrial net ecosystem CO2 exchange and underlying environmental constraints to productivity and respiration processes. http://www.ntsg.umt.edu/data http://www.ntsg.umt.edu/biblio




! 

  


 
 




 
 





"

" 

! ':0

/"/ ;0

$#/!/2 !'<0

*%(*;0

!#<0

$$%'=
$ 


  # 


 
 



 
  !
% 

 
!
& 

!
 !
' 

!


*'
'('
$*((
$#
)
($*'(
#
(
48:=5
$
'(%'
;0
=
($*'2(#
.#"(
#
$(.()"
%'$((
#
)

')
$

'#!#
4@?$ 0
?A$5
#
+!'2 $',.

4@A$ 0
:>$50
#
#
$')'#
!( 
)
)
$$!
 
!
))$#
4?A$ 0
:=B$51


#
!( 
(#
:BB=0
! '

"
+
#
*#') #
$(.()"
4)'
(0
%#$!$.0
+))$#
$"%$()$#3()'*)*'0
($!

.#"(0
),
%)0
($!
)"%')*'
#
"$()*'5
#
%!#)
%.($!$!
$!$.
4!2!+!
%$)$(.#)((
#
)'#(%')$#5
"(*'"#)(
!$#

(#$,
%)
'#)
-%'"#)
4%2<"0
#)'")2:1>"0
"#)291>
"
#
'*291;>
"5
*(#
(#$,
#(
#
$"#)$#
,)
"#)
#
(*""'
,'"#
4 
"'(51
'"'$()
(
#
),
#
)
%()
(#$,
/$#
.
*%
)$
<9
"1


8:=
+!*(
$
'(%'
;
#
$
;
%
#
)
($!
%'$!0
#)
))
($!

,
(
$+'
:>99
.%
(
#
$"%$(
)
%)
#
*(#
#
)$
)
"$'#
)"$(%'1

(%')$#
"(*'"#)(
#
;9:;
#)
))
)
($!
'(%')$#
')(
'
$#
+'
()
#
)
#)'")
(#$,
/$#
$"%'
)$
'(
))

!$,0
"#)
#
%
(#$,
)
%'$'
,#)'(
4*'
:51
#
)

')
$

'#!#0
! '


+
#
*#') #
,#)'
-%'"#)(
6(#$,
"#%*!)$#
4%2:1>
"0
#)'")2:
"
#
"#)291;>
"57
#
(*""'
,'"#
4),$
!+!(5
-
(*""'
,)'#
-%'"#)(
(#
;99<
&*#).#
)'
(0
+))$#0
#
($!

%'$(((
#
)
($*'(
#
(
$
'(%'
;1

(
+
#
# *#)$#
,)
$('+)$#!
()*(
$
'(%'
; ($*'(
#
(
#

')
%$!'
()'%
!#(%(
,'
($!

(
-
<90999
.%1

$'
)
%()
<
.'(0
,
+
#
*#') #
8:=
$
'(%'
;
"(*'"#)(
#
$
8:=2;
)'$*$*)
)
($!
%'$!
#
)
(*""'
,'"#
.
,)'#
-%'"#)1


+
$*#
))
)
%'$%$')$#
$
##)

"))
,!!

'
#

,'"'0
''

')
,!
"$'#

,!!
$"#)
$(.()"
'(%')$#
#

,'"'
#
,))'

')1
*'
##(
%)
)
"%$')#
$
(*""'
%'%))$#
$#
'*!)#
)
($*'(
#
(
$
(*""'
$(.()"
'(%')$#
#
)
#$')'!.
"$()
)''()'!
!#(%(
$#
')1
+!'0
$',.
(
,'
$*'
)"
(
()*.#
)
($*'(
#
(
$
,#)'
'(%'
;
#
'(%$#(
)$
!$#2)'"
%'
(#$,
#
)

')
4*'
;51

(*'"#)(
#
)
!!
$
;9:;
#)
))
$(.()"
'(%')$#
#
$"#)
.
"$'#

%$$!(0
(
'%$')
.
/"/
#
! '
;9:9
$'

'#!#1

)'
%!#)
(#(#0
'(%'
;0
8:=2+!*(
'
#)+
$
##)

($*'(
$#)'*)#
)$
'!.
,#)'

"(($#(1

(*'"#)(
$
)
($*'(
#
"#)*(
$
,#)'

!$(((
,!!
$#)#*
$#
+!'
)(
,#)'1

*'
:1
$!
'(%')$#
')(
)
$$!
 
"$()
*'
;1

#$,
#
-%'"#)
#
+#)!#0
+!'1
$!(
)*(($
)*#'
(#$,
"#%*!)$#
()1
C!$,
(#$,0
,!!

)
!$)$#
$
)
%
(#$,
/$#1

;
"
!$#
%!!'(
'
C"#)
(#$,0
C#)'")
(#$,0
 C
%
(#$,
))
)$
)
"'(0
))
!$#
)
%$!(1

*(0
%'$+#
'1
((
)$
)
"'

(%0
)
'(%'
;0
#
)$
)
($!
,!!(
,#
$+'
#
:1>
"
$
(#$,1 A Multi-scale, Multi-method Approach to Examining the Effects of Landscape Age on Dissolved Organic Carbon and Nitrogen Movement in Arctic Alaska K.A. Whittinghill1, J.C. Finlay, and S.E. Hobbie Introduction Figure 9: Dissolved organic carbon and nitrogen in soil or stream water Studies of terrestrial carbon (C) balance focus on gaseous Department of Ecology, Evolution and Behavior, University of Minnesota, St. Paul, MN 45 2.5 fluxes of C from ecosystems with little attention paid to Upslope DOC dissolved fluxes. Because dissolved organic C can be transported 40 Stream DOC and mineralized in aquatic systems, it is an important component 35 2 Upslope TDN of terrestrial C cycling. Given low concentrations of inorganic 30 Stream TDN 1.5 nutrients, dissolved organic matter (DOM) is particularly Figure 7: Effect of pH on dissolved organic nitrogen 25 important for plants and microbes in arctic ecosystems. 1) Does DOM production differ 2) Does soil geochemistry in experiment leachates 20 mg N/L However, little is understood about how known temperate mg C/L 1 0.14 geologic controls on DOM fluxes operate in the arctic. The among landscape ages? control DOM production? 15 10 Kuparuk River region in Alaska is a landscape mosaic with large 0.12 0.5 differences in glacial age on scales of less than one kilometer 5 (Figure 1, Table 1). Therefore, effects of landscape age on soil Figure 5: Hypothesis 0 0 Figure 3: Hypothesis 0.1 biogeochemistry can be studied in this region at sites with Itkillik III Itkillik II Itkillik I Sagavanirktok similar topography, climate, parent material and potential Increasing calcium or DOM production will 0.08 Age/extent of weathering vegetation. decreasing pH will increase as landscape Younger Older decrease DOM 0.06 age increases ProductionDOM Research Questions DOM Production production • Soil water DOC concentrations declined down slope; Age/extent of weathering 0.04 however, the magnitude depended on landscape age 1) Does dissolved organic matter production differ among High Ca High Ca Low Ca Low Ca (ANOVA: transect p=0.0004, age x transect p<0.0001). Low pH High pH Low pH High pH hillslopes of varying landscape age in arctic Alaska? 0.02

Difference in DON production • There were significant differences in stream water DOC and

Methods (mg N/g soil) (low pH - high pH) 2) Are landscape age associated differences in soil Methods 0 TDN concentrations among landscape ages; however, the geochemistry important controls on dissolved organic • We collected soil samples (n=3) from multiple sites on each of Itkillik II Itkillik I Sagavanirktok Anatuvuk magnitude of the effect depended on time since a rain event matter production in arctic terrestrial ecosystems? To examine landscape age related effects of calcium (Ca2+) and pH on (ANOVA: DOC age p=0.0135, DOC age x week p=0.0408, TDN age p=0.256, TDN four landscape ages during peak growing season (Table 1). There DOM production we conducted a laboratory incubation (same Age/extent of weathering age x week p=0.9558). 3) Do regional differences in soil dissolved organic matter were no significant differences in bulk density, thaw depth, or soil Younger Older method as Part I) with soils factorially manipulated to obtain the production translate into differences in dissolved organic moisture between landscape ages $129$Į   • Patterns in stream water DOC and TDN do not mirror following treatments: matter fluxes at the watershed scale? • pH had a significant effect on cumulative DON leached, but there patterns in laboratory DOC and DON production. o 2+ • We conducted a 6-month laboratory incubation at 4 C (mean 1) High Ca , low pH (pH=4.5) was no significant effect of Ca on leached DON (ANOVA pH: p<0.0001, • Hillslope position (transect) did not affect soil or stream growing season soil temperature). Each month we leached samples 2) High Ca2+, high pH (pH=6) Ca: p=0.4799). water TDN concentrations (ANOVA p=0.9220). Underlying Hypothesis with 90 mls of a micronutrient solution analyzing leachates for 3) Low Ca2+, low pH (pH=4.5) dissolved organic C (DOC), dissolved organic nitrogen (DON), 4) Low Ca2+, high pH (pH=6) • An interaction between landscape age and pH suggests other age related factors also control DON production (ANOVA: p=0.0003). We hypothesize older, more weathered sites with lower pH and nitrate, and ammonia. Landscape age and exchangeable cation concentrations will have higher rates of DOM production and transport due to stabilization of DOM by Figure 6: Cumulative dissolved organic carbon in Increasing pH decreases dissolved Topography Control Hillslope polyvalent cations in younger, less weathered soils (Figure 2). Figure 4: Cumulative dissolved organic carbon and experiment leachates by treatment nitrogen in incubation leachates organic nitrogen production DOM Concentrations Younger 1 0.2 Dissolved organic carbon Itkillik II 0.9 0.18 Toolik Lake Dissolved organic nitrogen 1.2 Itkillik I

0.8 0.16 Age Sagavanirktok 1 3)Are landscape age differences Conclusions 0.7 0.14 Anatuvuk Older 0.6 0.12 0.8 in DOM production apparent at • Because differences in soil pH and cation content 0.5 0.1 exist throughout the circumpolar region, differences in the watershed scale? DOM production caused by differences in pH and 0.4 0.08 0.6 mg C/g soil mg N/g soil exchangeable cations may have important consequences (Alaska PaleoGlacier Atlas) 0.3 0.06 mg C/g soil 0.4 We sampled growing season for tundra carbon budgets. 0.2 0.04 soil waterc DOC and DON concentrations across four (Toolik Field Station GIS) 6 km 0.2 0.1 0.02 hillslopes at top-, mid-, and • Transformations of DOM occur within arctic 0 0 0 bottom-slope locations hillslopes; however, patterns in aquatic export of DOM Figure 1: Glaciology of the Upper Kuparuk River Region Itkillik II Itkillik I Sagavanirktok Anatuvuk (Figure 8) weekly from July- high Ca high Ca low Ca low Ca in first order streams do not reflect differences in DOM Age/extent of weathering August 2005. production and transport within terrestrial hillslopes. low pH high pH low pH high pH Younger Older Each hillslope was located in a Table 1: Timeline of landscape ages in the Kuparuk River Region Treatment small watershed of distinct • Both topography and geochemical differences Vegetation Sites Sites Exchangeable Glaciation Age (kyr BP) pH • There was a trend towards lower cumulative DOC in Itkillik II landscape age where we also Type (2004) (2005) Cations associated with landscape age appear to be important samples than in Itkillik I, Sagavanirktok, or Anaktuvuk samples • Both Ca and pH had significant effects on cumulative leached sampled C and nutrient controls on arctic soil DOM cycling within hillslopes Itkillik III 11 to 13Moist 6.5 0 2 ȝPROHVFKDUJHJVRLO (ANOVA: p=0.0763). DOC in our experiment (ANOVA Ca: p=0.0014, ANOVA pH: p<0.0001) concentrations in a first order Itkillik II 11 to 50Nonacidic 6.5 4 2 0.85 stream. and should be included when modeling regional soil C Itkillik I 50 to 120 5 3 1 0.3 Moist Acidic • Cumulative leached DON was significantly lower on the youngest • An interaction between pH and landscape age suggests other age and N cycling in Northern Alaska. Sagavanirktok 120 to 600 4.5 4 3 0.2 Tundra landscape age than the older ages $129$S 7XNH\¶V+6'Į  related factors also control DOC production (ANOVA: p<0.0001) Figure 8: Tundra Hillslope Transects Anaktuvuk 4800 4.5 2 0 0.3 and averaged between 70-96% of total N for each landscape age. • DON was significantly correlated to DOC and this relationship was not significantly different among glacial drifts $1&29$Į   Both increasing calcium Acknowledgments concentrations and increasing We would like to thank our collaborators Edward Rastetter, Gus Shaver, As expected, dissolved organic Joe McFadden, Sandy Weisberg, and Paul Bloom for their help and advice pH decrease dissolved organic on this project. We would especially like to thank Megan Ogdahl, Jeff matter production increased with Eickhoff, and Jenna Cook for their help with field and laboratory work. We carbon production would also like to thank our funding sources: the Dayton-Wilkie Natural landscape age History Fund and the National Science Foundation. Figure 2: Stabilization of DOC through polyvalent cation “bridging”

1Please contact Kyle Whittinghill ([email protected]) for further information. KYLE ANN WHITTINGHILL Earth Systems Research Center and Department of Natural Resources and the Environment University of New Hampshire • 8 College Road • Durham, NH 03824 [email protected] • 651-247-3296

Research Interests: In my research I connect empirical work with ecosystem modeling to examine how biogeochemical processes in soils and sediments affect watershed fluxes of carbon and nutrients. My dissertation research examined how landscape variation in geochemistry affects decomposition and dissolved fluxes of carbon and nutrients at multiple spatial scales in arctic watersheds. My research demonstrated that landscape variation in geochemistry is an important control on decomposition and dissolved organic matter production in arctic organic soils. However, topography was a more important control on dissolved organic carbon concentrations in watersheds than landscape variation in geochemistry. Despite observed differences in concentrations of dissolved organic matter within hillslopes, there was no landscape variation in biodegradability of dissolved organic matter. In my first post doc, I combined field data from a long-term nitrogen fertilization experiment with an ecosystem model of forest biogeochemical cycling (TRACE) to examine mechanisms behind higher soil carbon storage under experimental nitrogen addition. This research demonstrates that increases in soil carbon storage are the result of decreased extent of decomposition, rather than slower decomposition rates. As part of a project examining the effects of changing seasonality on nutrient cycling in arctic stream networks, I have developed a reach-scale model of arctic stream carbon, nitrogen, and phosphorous cycling. I am currently preparing a manuscript using this model to examine potential changes in stream carbon and nutrient cycling with arctic climate change. This fall I also began work as part of a large, interdisciplinary project investigating how changing climate and land use in New England will affect water availability and watershed ecosystem services. The project includes terrestrial and aquatic ecosystem scientists and modelers as well as economists, policy experts, and educators. I will be responsible for integrating terrestrial (PnET) and aquatic (FrAMES) models to examine whole watershed fluxes of water, carbon, and nitrogen.

B13F-0639 Methane Bubbling From Three Arctic Lakes Martin Wik1, Patrick Crill1, Jo Uhlbäck1, David Bastviken1,2

1 Dept of Geological Sciences, Stockholm University, 106 91 Stockholm, Sweden. 2 Dept of Water and Environmental Studies, Linköping University, 581 83 Linköping, Sweden Contact: [email protected]

Introduction Results Daily average bubble fluxes, air pressure and temperature data Distribution, accumulated fluxes and local CH4 source comparisons

The amount of methane (CH4) emitted from northern lakes to the atmosphere is uncertain but is expected to increase due to 4500 -2 Inre, Mellan Harrsjön 800 2009 m

2700 4 arctic warming. A large portion of total lake CH4 emissions to the atmosphere is via ebullition (bubbling). Bubbling events are Villasjön 2010 temporally and spatially variable and make accurate measurements difficult to obtain. Ebullition is likely triggered by changes 200 600 2011 of biophysical drivers such as hydrostatic pressure, winds and temperature. During the ice free periods of 2009–2011, we meas- 300 ured ebullition in three lakes within the Stordalen mire, a landscape with degrading permafrost in arctic Sweden. We started 150 n = 5014 150 400 with two small lakes in 2009 using a large number of bubble traps that were deployed in different parts of the lakes and over vari- Count 100 ous depths. In 2010 and 2011, we included a third, larger but shallower lake, increased the number of traps and sampled more frequently, usually every day or every other day. Water temperatures Stordalen 200 50 02550

at different depths, air pressure and wind speed were measured during the entire time. Gas Cumulative flux, mg CH samples for stable isotope analysis were also retrieved regularly. Arctic circle 0 0 0-1 m 0 200 400 600 800 1000 160 180 200 220 240 260 -2 -1 Day of Year Bubble flux, mg CH4 m d (Left) Histogram showing the distribution of all measured bubble fluxes, i.e. the probability that any trapped bubbling event will Bubble trap measurements yield a specific flux magnitude. (Right) Cumulative bubble fluxes (all three lakes combined) for each of the three studied summers. Sweden 1-2 m t

5PUBM
PG

TZTUFNBUJDBMMZ
EJTUSJCVUFE
CVCCMF
USBQT
JO
UISFF
MBLFT t Inverted funnel design - 50 cm in diameter Mellan Harrsjön Inre Harrsjön t

&BDI
USBQ
JT
TUBCJMJ[FE
CZ
XFJHIUT
BOE
DPOOFDUFE
UP
B
NPPSJOH 2-3 m

t

.BOVBM
TBNQMJOH
PG
USBQQFE
HBT
VTVBMMZ
FWFSZ
o
IPVS Villasjön t


NFBTVSFE
ĘVYFT
EVSJOH
UISFF
TVNNFST 3-4 m Frequent bubbling

Inre Harrsjön 4-5 m Frequent bubbling 3-way Palsa stopcock Marked cylinder 5-7 m

Lake emissions Mellan Fen Sphagnum Harrsjön Photo: N. Rakos AirT 1.0 Mire emissions Ebullition Diffusion 0.1 3.0 -1 0.3 5.0 200 80 80 -1 d 0.5 7.0 d -2 Total TotalPlot 1 -2 m 150 60 60 m 4 4

100 40 40

50 20 20 50 cm Flux, mg CH 0 0 Flux, mg CH 0 2004-2007 2009 2010 2011 2010 2011

VillasjönVillasjön CH4 sources within the Stordalen Mire. The mire emissions are THC data (green season) reported by Bäckstrand et al. 2010. The diffusive lake fluxes were measured from June to September using floating chambers (Uhlbäck and Crill, unpublished data). Summer data from 2009 to 2011. The upper plots show the overall daily average CH4 bubble fluxes for each of the three lakes andhourly air pressure. Plotted 68°21’ N below are the fluxes at each depth interval, temperature data at different depths (m) from Mellan Harrsjön along with hourly airtemperatures.

19°02’ E 19°02’ Conclusions Depth dependence of ebullition t Large gas releases correlate with sudden air pressure drops after longer periods of increasing 900 pressure. 0 100 200 m Inre, Mellan Harrsjön -1 Villasjön d 100

-2 t Shallow sediments bubble more frequently during summer. Deep sediments start to bubble later, m 4 80 subsequent to the breakdown of the lakes' thermal stratifcation and warming at depth in late

Bubble traps Water depth (m) H Rock surface 0-1 4-5 summer/early fall. 60 Palsa 1-2 5-6 Villa t CH4 from shallow sediments have heavier isotopic signatures which indicate oxidation and/or 2-3 >6 40 3-4 different methanogenic pathways (acetate dissimilation rather than CO2 reduction). 20 Bubble flux, mg C t The amount of CH4 emitted through highly episodic ebullition is in the range of the diffusive flux The three lakes are situated within a dynamic landscape with sporadic discontinuous 0 and two- to fivefold lower than emissions from the the surrounding mire. permafrost, the Stordalen Mire, located 11 km east of Abisko in northern Sweden. 0-1 1-2 2-3 3-4 4-5 5-7 The mire complex consists of palsas, semi‐wet ombrotrophic areas and wet minero- trophic fens. Inre Harrsjön has an area of 0.023 km2 (2.3 ha) and a maximum depth Depth intervals, m Acknowledgments References of 5 m. Mellan Harrsjön is smaller and covers 0.011 km2 (1.1 ha) with a maximum depth of 7 m. Villa sjön is almost five times larger than Inre Harrsjön but is 1 m deep (Left) Average bubble fluxes (all three years combined) from various depths. (Right) δ13C signatures of the collected CH4 bubbles plotted against water depth. The Swedish research council (VR) has supported this research with grants to P. M. Crill. We are grateful to the Bäckstrand, K., P. M. Crill, M. Jackowicz-Korczynski, M. Mastepanov, staff at ANS (Abisko Scientific Research Station) for their continual support and to Jacqueline Amante, Kaitlyn T.R. Christensen, and D. Bstviken (2010), Annual carbon gas budget for and freezes to the bottom every winter. The solid circles are averages at each 1 meter depth interval. Error bars denote 1 SE. Steele, Niklas Rakos and Oskar Bergqvist for thier help in the field. a subarctic peatland, Northern Sweden, Biogeosciences, 7, 95-108.

Landscape Level Integration of Methane and Carbon Dioxide Exchange with a Thawing Permafrost Complex – Contribution of Lakes

Martin Wik

Phd student, Stockholm University, Sweden

The role of lakes and ponds has been overlooked in both the magnitude and the mechanism of the C remobilization from northern systems. My research project will improve our ability to quantitatively integrate land and lacustrine surface fluxes within a dynamic, climate sensitive ecosystem. The research builds upon prior work at the Stordalen Mire, a peatland/lake complex underlain by discontinuous permafrost in arctic Sweden.

The specific scientific goals are to:

1. quantify the magnitude and variability of CH4 and CO2 exchange at the landscape scale across a dynamic, thawing permafrost mire/lake ecosystem.

2. characterize the CH4 and CO2 stable isotopic signatures of the different reservoirs and flux components in order to derive process level understanding of the net emissions. 3. integrate the component sources of these data and their drivers into biogeochemical process level (DNDC) and landscape level models (the McGill Wetland Model).

Carbon accumulation in peatlands over the Holocene: a circum-arctic synthesis Zicheng Yu1, Dave Beilman2, Philip Camill3 & Julie Loisel1

1. Lehigh University, Bethlehem, PA 2. University of Hawai’i Manoa, Honolulu, HI 3. Bowdoin College, Brunswick, ME

THE PROBLEM TARGET REGIONS & AVAILABLE DATA Considerable uncertainty exists regarding the faith of peat-carbon stocks in a Green circles: We targeted these peatland regions for new data collection (A. warmer world because warming temperatures increase both plant net primary northern continental Alaska; B. Mackenzie River basin; C. western Hudson Bay production (carbon sink) and peat decomposition (carbon source). In this context, lowlands; D. Labrador; E. Kamchatka). We visited Kamchatka in August 2012 and documenting long-term peatland development and associated carbon accumulation retrieved 12 peat cores, 4 of which are currently being analyzed. histories allows for a better understanding of present and future peatland-carbon- climate interactions and feedbacks. Blue circles: We are hoping to compile about 100 Holocene peat records with the help of our collaborators, including new records from these three key peatland OUR PROJECT regions (F. Finland; G. western Siberia; H. eastern Hudson Bay lowlands). The main project objective is to synthesize all available circum-arctic (> 40N) peatland carbon records spanning the Holocene. The paleo-peat community has Black circles: Peatland sites with radiocarbon-dated basal peat. responded positively to our project, as over 40 investigators have agreed to send us Red squares: Existing paleo-temperature data will allow investigation of direct their datasets and participate in the synthesis effort. The project has 3 main phases: climate-peat carbon linkages. Yellow triangles: Sites from Yu et al. 2009’s peat-carbon compilation. Phase 1. Data compilation. We are gathering Holocene peat core records that were analyzed at 1cm intervals for organic matter density and whose chronologies were constrained by at least 5 dates (14C, tephra, pollen, etc.). New age-depth models will be developed using the software Bacon and will constitute the basis for calculating high-resolution peat-carbon accumulation rates. Short cores with 210Pb chronologies are also accepted to explore short-term peat accumulation dynamics. Finally, additional data such as stratigraphic information, plant macrofossils, or C, N, and P content are also being compiled and will allow us to perform additional crosschecks and pilot analyses.

Phase 2. Data synthesis. We will combine these datasets in 1000-yr bins (refer to Yu et al. 2009 and Charman et al. 2012 for examples) and run statistical analyses to examine spatial and temporal patterns of peat-carbon accumulation over the Holocene (the same will be performed for the past 100 years using 10-yr bins).

Phase 3. Virtual Peatland Center. We are developing an infrastructure to make peatland datasets publicly available. The web interface (“Virtual Peatland Center”) aims at facilitating data accessibility and encourage collaborative work within our community, as well as sharing our knowledge with other research groups such as modelers, ecologists, conservation groups, and others.

Yu et al. 2009: Sensitivity of northern peatland carbon dynamics to Holocene climate change, American Geophysical Union Monographs Series, 184, 55–69. Charman et al. 2012: Climate-related changes in peatland carbon accumulation during the last millennium, Biogeosciences Discussion, 9, 14327–14364.

ACKNOWLEDGEMENTS We acknowledge the support from NSF OPP Arctic Programs (ARC-1107981; ARC-1108116; ARC-1107628) and all our collaborators who are working with us on this community-wide effort.

Zicheng Yu Department of Earth and Environmental Sciences Lehigh University, Bethlehem, PA 18015 Phone: 610-758-6751; E-mail: [email protected]

Research Statement

Research in the Yu lab is in two main areas: Holocene peatland carbon dynamics using peat cores, and vegetation and paleoclimatic reconstructions mostly using lake sediments. Our peatland-related research mainly focuses on two inter-related areas: (1) synthesis and integration of carbon- accumulation processes and global carbon cycle significance of global peatlands (northern, tropical and southern peatlands) at the present and during the Holocene, and (2) high-latitude and high- altitude ecosystem responses to changes in insolation, climate and hydrology at various time scales, with fieldwork in Alaska, Tibetan Plateau, Patagonia, Kamchatka and the Antarctic Peninsula in the near future. With these research activities, the key questions we are asking include: (1) what controls peat accumulation in peat-accumulating ecosystems over various timescales? (2) how to best quantify global peat carbon stocks and their change over time? (3) what roles have peatlands played in the global carbon and oxygen cycles? The strategies to address these questions are (1) to generate case studies across the full spectrum of various peat-accumulating ecosystems in different continents/environments (Alaska, Kamchatka, Tibetan Plateau, Patagonia, and Antarctica), and (2) to generate global-scale and large-scale syntheses, as well as empirical and process-based modeling analyses.

Recent relevant publications: Jones, M.C. and Yu, Z.C. 2010. Rapid deglacial and early Holocene expansion of peatlands in Alaska. Proceedings of National Academy of Sciences USA, 107: 7347-7352. Hunt, S., Z.C. Yu, and M. Jones. 2012. Late-glacial and Holocene climate, disturbance and permafrost peatland dynamics on the Seward Peninsula, western Alaska. Quaternary Science Reviews (in press). Loisel, J. and Z.C. Yu. 2012. Recent acceleration of carbon accumulation in a boreal peatland, south-central Alaska. Journal of Geophysical Research – Biogeosciences (in press) doi:10.1029/2012JG001978 Spahni, R., F. Joos, B.D. Stocker, M. Steinacher, and Z.C. Yu. 2012. Transient simulations of the carbon and nitrogen dynamics in northern peatlands: from the Last Glacial Maximum to the 21st century. Climate of the Past Discussions 8: 5633–5685. Yu, Z.C. 2011. Holocene carbon flux histories of the world’s peatlands: Global carbon-cycle implications. The Holocene 21: 761-774. Yu, Z.C. 2012. Northern peatland carbon stocks and dynamics: a review. Biogeosciences 9: 4071– 4085. doi:10.5194/bg-9-4071-2012 Yu, Z.C., Loisel, J., Brosseau, D.P., Beilman, D.W. and Hunt, S.J. 2010. Global peatland dynamics since the Last Glacial Maximum. Geophysical Research Letters 37, L13402, doi:10.1029/2010GL043584.

Carbon Stocks in Permafrost-Affected Soils of the Lena River Delta

Sebastian Zubrzycki1*, Lars Kutzbach1, Guido Grosse2, Alexey Desyatkin3, and Eva-Maria Pfeiffer1 1Institute of Soil Science, KlimaCampus, University of Hamburg, Hamburg, Germany *[email protected] 2Geophysical Institute, University of Alaska Fairbanks, Fairbanks, Alaska, USA 3Institute for Biological Problems of Cryolithozone, Siberian Branch of the Russian Academy of Sciences, Yakutsk, Russia

Goal:

Quantifying the soil organic carbon stocks (SOC) of the rarely investigated currently permanently frozen layers up to 1 m depth of the Lena River Delta’s SamoylovSa Island: ssoilo investigations and mapping Holocene river terrace and the active floodplains. (summer(s expedition) CalculationsC of ρ and based on it OC SOC(100 cm) for the top 100 cm Background: sampling of frozen soil coress The SOC in permafrost soils of north-east Siberia is known to be (spring expedition)) significant but is insufficiently investigated so far. Since carbon deposits in permafrost regions are likely to become a future carbon source, more detailed investigations of the Determination of the SOC(100 cm) presently frozen likely carbon-rich soil layers in arctic delta for the Holocene river terrace and active floodplains regions are of importance. Estimation of total SOC pools for the Holocene river terrace and the active floodplains of Lena River Delta Results:

- mean bulk densities ρd varied among the different soil layers from 1.0 to 1.5 g cm-3 and from 0.2 to 0.9 g cm-3 Landsat-7 ETM+ remote sensing image mosaic classification and within the active floodplain soils and river terrace soils, subsequent accuracy assessment respectively; with very high resolution WorldView-1 data (©DigitalGlobe) - gravimetric contents of organic carbon cOC showed a high scatter ranging from 0.17 to 42.46 % in the soils of the Holocene river terrace and ranging from 0.13 to Lena River Delta (22,000 km²) Holocene river terrace (4,760 km²): SOC pool 121 ± 43 Tg 27.71% in the soils of the active floodplain levels ; Active floodplains (8,830 km²): SOC pool 120 ± 66 Tg - soil organic carbon pool estimates for a depth of 1 m in the Holocene river terrace and the active floodplain soils of the Lena River Delta are 121 Tg C and 120 Tg C, respectively.

Organic Carbon and Total Nitrogen Stocks in Soils of the Lena River Delta

S. Zubrzycki1, L. Kutzbach1, G. Grosse2, A. Desyatkin3, E.-M. Pfeiffer1 [1]{Institute of Soil Science, KlimaCampus, University of Hamburg, Hamburg, Germany} [2]{Geophysical Institute, University of Alaska Fairbanks, Fairbanks, Alaska, USA} [3]{Institute for Biological Problems of Cryolithozone, SB RAS, Yakutsk, Russia}

Correspondence to: S. Zubrzycki ([email protected])

Abstract The Lena River Delta, which is the largest delta in the Arctic, extends over an area of 11 32,000 km² and likely holds more than half of the entire soil organic carbon mass stored in the 12 seven major deltas in the northern permafrost regions. The geomorphic units of the Lena 13 River Delta which were formed by true deltaic sedimentation processes are a Holocene river 14 terrace and the active floodplains. Their mean soil organic carbon stocks for the upper 1 m of 15 soils were estimated at 29 kg m-2 ± 10 kg m-2 and at 14 kg m-2 ± 7 kg m-2, respectively. For the 16 depth of 1 m, the total soil organic carbon pool of the Holocene river terrace was estimated at 17 121 Tg ± 43 Tg, and the soil organic carbon pool of the active floodplains was estimated at 18 120 Tg ± 66 Tg. The mass of soil organic carbon stored within the observed seasonally 19 thawed active layer was estimated at about 127 Tg assuming an average maximum active 20 layer depth of 50 cm. The soil organic carbon mass which is stored in the perennially frozen 21 ground below 50 cm soil depth, which is excluded from intense biogeochemical exchange 22 with the atmosphere, was estimated at 113 Tg. The mean nitrogen (N) stocks for the upper 23 1 m of soils were estimated at 1.2 kg m-2 ± 0.4 kg m-2 for the Holocene river terrace and at 24 0.9 kg m-2 ± 0.4 kg m-2 for the active floodplain levels, respectively. For the depth of 1 m, the 25 total N pool of the river terrace was estimated at 4.8 Tg ± 1.5 Tg, and the total N pool of the 26 floodplains was estimated at 7.7 Tg ± 3.6 Tg. Considering the projections for deepening of the 27 seasonally thawed active layer up to 120 cm in the Lena River Delta region within the 21st century, these large carbon and nitrogen stocks could become increasingly available for 1 decomposition and mineralization processes.