Transcript Document 7541697

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
Outline
1. Background of Carbon Cycle
2. What we concern about
3. Model and Data
4. Results and discussion
5. Conclusion
6. Future plan
d(co2)/ dt
Carbon Dioxide in the atmosphere has been steadily rising since regular
measurements began in 1958. The graph above shows both the longterm trend and the seasonal variation.
http://earthobservatory.nasa.gov/Library/CarbonCycle/carbon_cycle3.html
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
What we know and don’t make sure
 Bacastow (1976) firstly noticed the relation between CO2 and ENSO.
 Ocean-atmosphere flux variation is relative modest (Feely 1987;Winguth 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.
The questions we concern:
 What’s 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?
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/ )
The VEgetation-Global Atmosphere-Soil Model (VEGA
Atmospheric
CO2
Photosynthesis
Carbon
allocation
Autotrophic
respiration
4 Plant Functional Types
Broadleaf tree
Needleleaf tree
C3 Grass (cold)
C4 Grass (warm)
3 Vegetation carbon poo
Leaf
Root
Heterotrophic
Wood
respiration
Turnover
3 Soil carbon pools:
Fast
Intermediate
Slow
GPP = NPP + Ra
NEP = NPP - Rh
NEE = - NEP
GPP
Total carbon pool
Ra
Cvege
Csoil
CLeaf
+ Cwood
+ Croot
Csfast
+ Csmed
+ Csslow
Cleaf (15)
Rh
Cwood (605)
Croot (21)
Fast soil ( 307)
Med soil ( 610)
Slow soil ( 931 )
Carbon Pool (GtC) and Flux
Concept of VEGAS
GPP = NPP + Ra
NEP = NPP - Rh
NEE = - NEP
GPP (124.43)
rspgrow ( 37.33)
rspleaf ( 9.11)
Growth
burnleaf(0.25) +
Leaf
Wood
Root
( 28.72)
( 38.04)
( 20.30)
rspsslow ( 0.60)
( 37.33)
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)
tovfireleaf(0.0) +
tovfirewood(4.88)
tovwood( 20.16)
tovroot( 10.66)
Stswood( 6.05)
Stsroot( 0.21)
Fast soil
Med soil
tovsfast ( 3.84)
Carbon Flux ( Gtc/yr)
tovsmed( 0.61)
Slow soil
erosfast ( 0.407)
erosmed( 0.064)
erosslow ( 0.010)
1. Land-Atmos Flux & co2 growth rate
 Land-Atmos Flux & Ocean –Atmos Flux
Note: Ocean-Atmos flux from HAMOCC5
http://www.mad.zmaw.de/Models/UbersichtvorgModelle/HAMOCC5.html
2. Regional ENSO composite
NEE
NEE=Rh - NPP
Prec / Ts
Prec/Swet/GPP
NDVI/LAI
Ts/Rh
Global
Tropics( 20S -20N)
NH2090: 20N-90N
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
ENSO composite Spatial evolution(2)
NEE= Rh - NPP
3. ENSO, El Niño and La Nina composite features
ENSO
 Decay speed
 Lag with -SOI
El Niño
La Nina
4. Lags correlation with –SOI
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.
NEE
NPP
Rh
Trop:
Control
1) NPP:Rh = 3:1
Prec - only
Ts_only
2) Prec:Ts = 1:1
PrecNPP
Ts NPP/Rh
Swet-fix
Inverse
3) Swet  NPP
NEE(4)=NPP(3)+Rh(1)
NPP(3): Prec(2)+Ts(1)
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.
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.
Reference
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Zeng, N., A. Mariotti, and P. Wetzel, 2005: Terrestrial mechanisms of interannual CO2
variability, Global Biogeochemical Cycles, 19, GB1016, doi:10.1029/2004GB002273
Zeng, N., H. Qian, E. Munoz, and R. Iacono (2004), How strong is carbon cycle-climate
feedback under global warming? Geophys. Res. Lett., 31 L20203,
doi:10.1029/2004GL020904.
Zeng, N., H. Qian, C. Roedenbeck, and M. Heimman, 2005: Impact of 1998-2002
midlatitude drought and warming on terrestrial ecosystem and the global carbon cycle. GRL.
Potter C, Klooster S, Steinbach M, et al.2003 Global teleconnections of climate to terrestrial
carbon flux JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES 108 (D17).
Hashimoto H, Nemani RR, White MA, et al 2005. El Ni(n)over-tildeo-Southern Oscillationinduced variability in terrestrial carbon cycling JOURNAL OF GEOPHYSICAL
RESEARCH-ATMOSPHERES 109 (D23)
Cao, M. K. and Woodward, F. I. 1998. Dynamic responses of terrestrial ecosystem carbon
cycling to global climate change. Nature 393, 249–252.
Cao, M. K., Prince, S. D. and Shugart, H. H. 2002. Increasing terrestrial carbon uptake from
the 1980s to the 1990s with changes in climate and atmospheric CO2. Global Biogeochem.
Cycles 16, 1069
Houghton, R. A. 2003. Why are the estimates of the terrestrial carbon balance so different?
Global Change Biol. 9, 500–509.
Gurney, K. R., Law, R. M., Denning, A. S., Rayner, P. J., Baker, D. and coauthors. 2002.
Towards robust regional estimates of CO2 sources and sinks using atmospheric transport
models. Nature 415, 626–630.
Cramer W, Field CB Comparing global models of terrestrial net primary productivity (NPP):
introduction GLOBAL CHANGE BIOLOGY 5: III-IV Suppl. 1 APR 1999