Transcript Slide 1

AGU2012-GC31A963: Model Estimates of Pan-Arctic Lake and Wetland Methane
Emissions
X.Chen1, T.J.Bohn1, M. Glagolev2, S.Maksyutov3, and D. P. Lettenmaier1
1University
of Washington, Seattle, Washington, USA ; 2Moscow State University, Moscow, Russia; 3National Institute for Environmental Studies, Tsukuba, Japan
San Francisco, December 5, 2012
Contact: [email protected]
3. Model Calibration – over West Siberia
Lakes and wetlands are important sources of the greenhouse gases CO2 and CH4, whose emission rates are sensitive to
climate. The northern high latitudes, which are especially susceptible to climate change, contain about 50% of the world’s
lakes and wetlands. With the predicted changes in the regional climate for this area within the next century, there is concern
about a possible positive feedback resulting from greenhouse gas emissions (especially of methane) from the region’s
wetlands and lakes. To study the climate response to emissions from northern hemisphere lakes and wetlands, we have
coupled a large-scale hydrology and carbon cycling model (University of Washington’s Variable Infiltration Capacity model;
VIC) with the atmospheric chemistry and transport model (CTM) of Japan’s National Institute for Environmental Studies and
have applied this modeling framework over the Pan-Arctic region. In particular, the VIC model simulates the land surface
hydrology and carbon cycling across a dynamic lake-wetland continuum. The model includes a distributed wetland water
table that accounts for microtopography and simulates variations in inundated area that are calibrated to match a passive
microwave based inundation product. Per-unit-area carbon uptake and methane emissions have been calibrated using
extensive in situ observations. In this paper, the atmospheric methane concentrations from a coupled run of VIC and CTM
are calibrated and verified for the Pan-Arctic region with satellite observations from Aqua’s Atmospheric Infrared Sounder
(AIRS) and Envisat’s Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY) instruments.
We examine relative emissions from lakes and wetlands, as well as their net greenhouse warming potential, over the last
half-century across the Pan-Arctic domain. We also assess relative uncertainties in emissions from each of the sources.
6. Surface Methane Concentration
Comparison of AIRS and Sciamachy kernels
•Saturated extent calibrated to
match classifications of
ALOS/PALSAR (synthetic
aperture radar) imagery
(courtesy of NASA/JPL)
1. Research Domain
•CH4 fluxes calibrated to match
in situ observations of Glagolev
et al (2011)
Northern High Latitudes: 45N-80N, 180W-180E
4. Wetland Methane Emissions over West Siberia
Annual average methane emissions, 2001-2010
Glagolev et al. (2011):
3.9 +/- 1.4 Tg CH4/y
0
Pressure Level (mb)
•Inundated extent calibrated to
match the daily AMSR-E-based
(passive microwave) product of
Schroeder et al (2010)
SCIAMACHY kernel: sensitive to
whole atmospheric methane
concentration, but most sensitive
to near surface concentration
SCIA
200
AIRS
400
AIRS kernel: sensitive to upper
layer atmospheric methane
concentration
600
800
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1000
Note: for current simulation, wetlands
methane emission is mainly from West
Siberia.
Kernel Value
SCIA_kernel
AIRS_kernel
DIFFERENCE
Observation
Abstract
Our Estimate: 3.6 Tg
CH4/y (1.7-6.0 Tg CH4/y)
Global Lake and Wetland Database
(GLWD), Lehner and Döll (2004)
2. Modeling Framework
1.
2. VIC’s dynamic lake-wetland continuum
3. Linkages among models
Simulation
•Lakes and wetlands are the world’s largest natural source of CH4, a powerful greenhouse gas
•Their CH4 emissions are sensitive to climate factors such as temperature and precipitation
•Nearly 50% of the world’s lakes and wetlands occur in the northern high latitudes, North of 45 N
•The high latitudes have experienced pronounced climate change over the last half-century, and are
projected to continue doing so through the next century
•There is concern that future changes in lake and wetland CH4 emissions could produce a
substantial feedback to climate change
•Uncertainties are large, due to sparse observations
•Can we use a combination of remote sensing and process-based models to monitor CH4
emissions and reduce uncertainty?
Our modeling framework matches both the total magnitude and
approximate spatial distribution of the estimate of Glagolev et al (2011).
5. Simulated Atmospheric Concentrations over W. Siberia
•Simulated emissions computed in section 4 (above) are input to the CTM model
•As an example, simulations using 3 different lake CH4 emission rates are shown below
•AIRS satellite [CH4] observations are sensitive primarily to middle-upper troposphere
[CH4]
•To compare with AIRS, our simulated [CH4] has been convolved with the AIRS
sensitivity kernel
•Resulting mid-upper-troposphere [CH4]:
CTM, Lake CH4 = 10
mg CH4/m2/day
1. Simulate land surface hydrology and carbon cycle using Variable Infiltration Capacity (VIC)
model
2. VIC’s dynamic lake-wetland model impounds surface water, allowing lakes to expand and
flood wetlands; within exposed wetlands, water table is distributed according to
microtopography
3. Distributed water table is input to Walter-Heimann (2000) wetland methane emissions
model; total grid cell methane fluxes are input to NIES Atmospheric Chemistry Tracer
Transport Model (CTM)
CTM, Lake CH4 =
250 mg CH4/m2/day
CTM, Lake CH4 =
500 mg CH4/m2/day
AIRS [CH4], JJA 2003-2010
• Difference of two kernels on model output shows correctly the high emission areas at West
Siberia and northern Canada, as expected
• The difference of observations (SCIAMACHY and AIRS) reflects the high-emission areas
(north America, central Eurasia), but does not match the model output well. WHY?
 Daily variation of atmospheric methane concentration. Two satellite observations are at
different times of the day
 Inaccuracy of satellite observation data compared to actual atmospheric methane
concentration. Lower resolution of AIRS observation would make the difference more biased.
Possible solution: downscaling of AIRS observation data
 Lack of emission from northern Canada.
7. Conclusions
1. VIC model successfully reconstructed lake and wetlands topography, and gave reasonable
variation of water tables over the whole Pan-Arctic region
2. CTM model reproduced the lower atmospheric methane concentration patterns well. Coupling
of hydrological model (VIC) and atmospheric model (CTM) could show atmospheric methane
concentration well where hydrological cycle and carbon cycle are highly integrated
•Because CTM is poorly constrained at these altitudes, and because CTM’s resolution is so
coarse (2.5 degree), it is difficult for us to reproduce the AIRS spatial pattern in upper
troposphere with the CTM simulations. Is there a way to focus on near-surface [CH4]?
3. Future work: 1) Analysis of satellite observation of methane, finding a better way of deriving
near surface methane concentration from observations; 2) Conduct CTM runs using surface
methane emissions from all possible source from VIC output (i.e. West Siberia and northern
Canada)