The relationship between variation of terrestrial carbon cycle and ENSO

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The relationship between variation of terrestrial
carbon cycle and ENSO
Haifeng Qian 05/10/2006 Department of Atmospheric
Oceanic Science University of Maryland Advisor
Prof. Ning Zeng
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Outline

• Background of Carbon Cycle
• What we concern about
• Model and Data
• Results and discussion
• Conclusion
• Future plan

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d(co2)/ dt
Carbon Dioxide in the atmosphere has been
steadily rising since regular measurements began
in 1958. The graph above shows both the long-term
trend and the seasonal variation.
http//earthobservatory.nasa.gov/Library/CarbonCy
cle/carbon_cycle3.html
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In any given year, tens of billions of tons of
carbon move between the atmosphere, hydrosphere,
and geosphere. Human activities add about 5.5
billion tons per year of carbon dioxide to the
atmosphere. The illustration above shows total
amounts of stored carbon in black, and annual
carbon fluxes in purple. http//earthobservatory.
nasa.gov/Library/CarbonCycle/carbon_cycle4.html
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What we know and dont make sure

• Bacastow (1976) firstly noticed the relation
  between CO2 and ENSO.
• Ocean-atmosphere flux variation is relative
  modest (Feely 1987Winguth et al.1994 Francy
  et al. 1995 Bousquet et al. 2000 Roedenbeck
  et al. 2003 Zeng et al. 2005)
• Inverse modeling (Schimel et al. 2001 Gurney et
  al.,2002 Houghton 2003) long term sink and
  source regional uncertainties.
• Potter et al. did statistical analysis of ENSO,
  NAO with modeled land_atmosphere flux.
• Hashimoto et al. (2004) proposed that NPP is
  related to ENSO. Cao et al. (2005) modeled year
  to year variation of NEP up to 2.5 PgC/yr, in
  which 1.4 PgC/yr can be attributed to ENSO cycle
• Generally, on regional scale, there are still
  many uncertainties in mechanisms of climate
  controlling terrestrial carbon cycle.

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The questions we concern

• Whats kind of terrestrial carbon cycle in
  response to ENSO cycle.
• What are their common features during ENSO
  cycle?
• How do the climate factors control carbon
  exchange between land and atmosphere?

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Model and Data

• The VEgetation-Global Atmosphere-Soil Model
  (VEGAS) (Zeng 2003) and Land surface
  model(S_Land)( Zeng 2000) 2.5x2.5
• Climate forcing
• 1. Observed precipitation and
  Temperature (CRU, GISS, CMAP)
• 2. Seasonal climatology of
  radiation, humidity, wind speed
• 3. Atmospheric co2 is kept constant
  at preinustrial level
• Manua Loa atmospheric co2 (http//www.cmdl.noaa
  .gov)
• Roedenbeck inverse data (Max-Planck-Institut
  für Biogeochemie )
• NDVI data ( http//islscp2.sesda.com/ )

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The VEgetation-Global Atmosphere-Soil Model
(VEGAS)
4 Plant Functional Types Broadleaf
tree Needleleaf tree C3 Grass (cold) C4 Grass
(warm)
3 Vegetation carbon pools Leaf Root Wood
Carbon allocation
Heterotrophic respiration
Turnover
3 Soil carbon pools Fast Intermediate Slow
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GPP NPP Ra NEP NPP - Rh NEE - NEP
GPP
Ra
Cleaf (15)
Rh
Cwood (605)
Croot (21)
Fast soil ( 307)
Med soil ( 610)
Slow soil ( 931 )
Concept of VEGAS
Carbon Pool (GtC) and Flux
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GPP NPP Ra NEP NPP - Rh NEE - NEP
GPP (124.43)
rspgrow ( 37.33)
rspleaf ( 9.11)
Leaf ( 28.72)
Wood ( 38.04)
Root ( 20.30)
Growth ( 37.33)
rspsslow ( 0.60)
burnleaf(0.25) burnwood(2.85)
rspwood ( 4.13)
rspsmed ( 3.16)
Fire burning Wood Leaf (7.98)
rsproot( 9.43)
rspsfast ( 56.46)
tovleaf( 14.87) Stsleaf( 4.49)
firesfast( 0.62)
Fast soil
tovwood( 20.16) Stswood( 6.05)
tovroot( 10.66) Stsroot( 0.21)
tovfireleaf(0.0) tovfirewood(4.88)
erosfast ( 0.407)
Med soil
tovsfast ( 3.84)
erosmed( 0.064)
Slow soil
Carbon Flux ( Gtc/yr)
tovsmed( 0.61)
erosslow ( 0.010)
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1. Land-Atmos Flux co2 growth rate
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2. Regional ENSO composite
NEE
NEERh - NPP
Prec / Ts
Prec/Swet/GPP
NDVI/LAI
Ts/Rh
Global
Tropics( 20S -20N)
NH2090 20N-90N
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ENSO composite Spatial evolution(1)
Note here we assume that October in ENSO
composite is the maturity month of ENSO, so
negative value is leading month, positive is lag
month
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ENSO composite Spatial evolution(2)
NEE Rh - NPP
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3. ENSO, El Niño and La Nina composite features

• Decay speed
• Lag with -SOI

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4. Lags correlation with SOI
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5. Sensitivity simulations
To elucidate and quantify the effects of
climate factors in controlling the ecosystem, we
design other 3 sensitivity simulations as follows
Control Precipitation and temperature forcing use observation data. (specified before)
Prec-only Precipitation forcing same as Control, while temperature uses seasonal climatology .
Ts-only Temperature forcing same as Control, while Precipitation uses seasonal climatology .
Swet-fix The same as Ts-only except we fix soil wetness using its seasonal climatology
Then, we will do ENSO composite for each
sensitivity simulation and compare NEE , NPP, Rh
anomalies.
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NEE
NPP
Rh
Trop 1) NPPRh 31
2) PrecTs 11
Prec?NPP Ts ?NPP/Rh 3) Swet ? NPP

Control
Prec - only
Ts_only
Swet-fix
Inverse
NEE(4)NPP(3)Rh(1) NPP(3) Prec(2)Ts(1)
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Conclusion and discussion

• Interannual variability of atmospheric co2 growth
  rate at Mauna Loa is strongly correlated with
  ENSO signals with about 6 months lags
• VEGAS and inverse simulation generally agree
  well. Tropics plays the dominant role. In the
  extrotropics, the situation is more complicate
  due to weaker response to ENSO and regional
  cancellation.
• Global NEE anomaly tends to lag SOI about 7-8
  months, 6 months in the tropics. Inverse results
  show a little less lag. Lag correlation analysis
  are consistent with ENSO composite analysis.
• The tropical robust response to ENSO is caused
  by conspiracy of NPP and Rh anomalies induced
  by climate factors.
• The sensitivity simulations suggest in the
  tropics, temperature not only determine Rh, but
  also has the indirect effect on NPP through soil
  wetness. Temperature and precipitation effect are
  comparable in the tropics.
• NDVI shows general agreement with LAI in the
  extrotropics, while poor in tropics.

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Future plan

• In last 2 decades, there is a greening in high
  latitudes, which implies long term of sink(?),
  but Rh has increased by warming in high latitude.
• In middle and high latitudes, is it possible
  that other climatic index has statistically
  correlated to the interannual and multidecadal
  variation of sink and source.
• Land use effect and radiation/co2 effect on the
  photosynthesis
• Inter-comparison with other model output.

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Reference

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  Terrestrial mechanisms of interannual CO2
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