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Spatial and Temporal Patterns of Carbon Exchanges between the Atmosphere and
Terrestrial Ecosystems of China
Hanqin Tian
Ecosystem and Regional Studies Group
Auburn University, AL 36849, USA &
NASA IDS Project Participants*
*The NASA IDS Project Participants:
• Jerry M. Melillo, David Kicklighter, Ben Felzer, The Ecosystem Center, Marine Biological Laboratory, Woods Hole, USA.
• Steven Running, Maosheng Zhao, Qiaozhen Mu, University of Montana, Missoula, USA.
• Jiyuan Liu, Guoyui Yu, Aifeng Lv, Chaoqun Lv, Wei Ren, Xiaofeng Xu, IGSNRR, Chinese Academy of Sciences, Beijing, 100101, China.
• Ranga Myneni, Yuri Knyazikhin, Nikolay Shabanov, Boston University, Boston, USA.
• Mingliang Liu, Shufen Pan, Hua Chen, Siqing Chen, Guangsheng Chen, Chi Zhang, Auburn University.
Sink and Source of CO2 by Regions as Estimated by Inverse Modeling Approach
Investigators & Sources
North America
Eurasia
Tropics
Fan et al. (1998) (CMC, USA)
1.7 ± 0.5
0.1 ± 0.6
-0.2 ± 0.9
Rayner et al. (1999)
(CRCSHM, Australia)
0.5
0.2
0.0
Bousquet et al. (1999)
(LSCE, France)
0.5 ± 0.6
1.8
-1.1
– The US carbon sink: Forest inventory, bookkeeping model--land use change, and inverse model (Birdsey and Health, 1995, Houghton et al. 1999, Capersen et al. 2000, Pacala et al. 2001) (0.3-0.58 Pg/yr).
– The Eurasian carbon sink has received less attention.
IPCC TAR (Prentice et al. 2001): both North America and Eurasia served as carbon sinks during the 1990s, suggesting a net sink of 1-2.5 Pg C yr-1 that is distributed relatively evenly between North America and Eurasia.
From both scientific and policy perspectives, it is of critical importance to quantify regional carbon budget and mechanisms controlling the carbon cycle.
We haven’t succeeded in answering all problems. The answers we have found only serve to raise a whole set of new questions. In some ways, we feel we are as confused as ever, but we believe we are confused on a higher level and about more important things.
Everything about China, good or bad, is BIG!
China is the world’s third largest country, the most rapidly developing nation and home to 1.3 billion people. Since the early 1980s, the unprecedented combination of economic and population growth has led to a dramatic land transformation across the nation. China is “Natural Laboratory” for studying dynamics of coupled natural and human systems as well as the carbon cycle.
Population distribution of 2000 in China(cell size = 1 km, Unit: persons per km2)
0 – 2
2-5
5-10
10-30
30-50
50-80
80-100
100-200
200-300
300-400
400-500
500-600
600-700
700-800
800-900
900-1000
1000-1100
1100-1200
1200-1300
1300-1400
1400-1500
1500-1600
1600-1700
1700-1800
1800-1900
1900-2000
2000-2200
> 2200
1950 1970 1978 1990 2000 2002 2020
1317 18
27
3537
60
Ch
ines
e P
op
ula
tio
n u
rba
niz
ed
(%
)
Rapid urbanization in China
Liu, J., H.Q. Tian, M. Liu, D. Zhuang, J.M. Melillo and Z. Zhang (2005). Geophys. Res. Lett., 32, L02405, doi:10.1029/2004GL021649.
Large-scale land transformation estimated with satellite data
OBJECTIVES:
Our study will be organized by two linked questions:
Q1 - HOW HAVE PRIMARY PRODUCTION AND CARBON STORAGE CHANGED IN CHINA OVER THE PAST TWO DECADES?
Q2 - WHAT MECHANISMS HAVE HAD MAJOR EFFECTS ON CHANGES IN THESE FLUXES AND STOCKS? We will consider the relative roles of: (a) climate variability, (b) changes in land cover and use, (c) changes in fire disturbance, (d) changes in the chemistry of precipitation (particularly nitrogen), and (e) changes in the composition of the atmosphere (carbon dioxide, ozone).
APPROACHES:
Here we try to combine remote-sensing data (MODIS, AVHRR, Landsat-TM/ETM) and a set of biogeochemical simulation models (TEM, Biome-BGC and A new model) to quantify the consequences of land transformations and other environmental changes on productivity in forests and other “natural” ecosystems and carbon sequestration.
Satellite Data
Eddy Flux
Ecosystem Experiments
Flask Data
MeasuringModeling
Synthesis
The Integrated Approach Quantifying Regional C Dynamics
Data Development• Climate
• Land use history
• O3
• CO2
• Other data: elevation, soil texture, potential vegetation
Simulation Experiments
1. Climate Only
2. CO2 only3. Land use only
4. O3 only5. Climate + Land use
6. Climate + CO2 + Land Use
7. Climate + CO2 + O3
8. Climate + CO2 + O3 + Land use
TEM-based estimate on vegetation and soil carbon in 2000 (Climate_CO2_LUCC_O3)
Vegetation CarbonSoil Organic Carbon
gC/m2gC/m2
Mean annual Net Carbon Exchange (1981-1990) (gC/m2/yr)
Mean annual Net Carbon Exchange (1991-2000) (gC/m2/yr)
-0.8
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.619
80
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
Climate_CO2_LUCC_O3
LUCC only
Climate only
CO2 only
O3 only
Annual Net Carbon Exchange (PgC/yr)
-16.0
-14.0
-12.0
-10.0
-8.0
-6.0
-4.0
-2.0
0.0
2.0
4.0
6.0
1860
1865
1870
1875
1880
1885
1890
1895
1900
1905
1910
1915
1920
1925
1930
1935
1940
1945
1950
1955
1960
1965
1970
1975
1980
1985
1990
1995
2000
CO2 only
LUCC only
O3 only
Climate only
Climate_CO2_LUCC_O3
Cumulative Net Carbon Exchange (Pg C)
a) Mean AVHRR NPP from 1982-2000 b) NPP trend from 1982-2000 c) Mean MODIS NPP d) Mean NPP as estimated by a new model.
a. b. c.
d.
Summary• The combined effect of climate, CO2, land use and O3 on net carbon exchange show that terrestrial China acted as a small carbon sink ( 64 Tg C per year) during 1980-2000, but showing substantial year-to-year variation.
• For the time period from 1980 to 2000, both land use and CO2 resulted in carbon uptake while climate and O3 led to carbon release. During 1860-2000, however, land-use change resulted in a large release of carbon to the atmosphere (about 12 Pg C).
• CH4 emission from agricultural land varied from 6 to 16 Tg C per year. Fire-induced carbon emission is about 11.3 Tg C per year.
• In any year over the period 1980-2000, net carbon exchange can be very large in one location but very small or negative in another location because of the spatial heterogeneity of vegetation, soils and climate.
• Additional factors needed to be considered include N deposition, Forest management and agronomic practices.
• In the future, Model intercomparison needs to be done. Also modeled results need to be evaluated against field data.