The relationship between variation of terrestrial carbon cycle and ENSO

1. 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

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

3. 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/CarbonCycle/carbon_cycle3.html

4. 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

5. 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.

6. 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?

7. 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/ )

8. Atmospheric CO2 Photosynthesis Autotrophic respiration 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

9. 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

10. 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) burnleaf(0.25) + burnwood(2.85) rspsslow ( 0.60) 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)

11. Land-Atmos Flux & Ocean –Atmos Flux Note: Ocean-Atmos flux from HAMOCC5 http://www.mad.zmaw.de/Models/UbersichtvorgModelle/HAMOCC5.html 1. Land-Atmos Flux & co2 growth rate

12. 2. Regional ENSO composite NEE NEE=Rh - NPP Prec / Ts Prec/Swet/GPP NDVI/LAI Ts/Rh Global Tropics( 20S -20N) NH2090: 20N-90N

13. 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

14. ENSO composite Spatial evolution(2) NEE= Rh - NPP

15. ENSO El Niño La Nina 3. ENSO, El Niño and La Nina composite features • Decay speed • Lag with -SOI

16. 4. Lags correlation with –SOI

17. 5. Sensitivity simulations To elucidate and quantify the effects of climate factors in controlling the ecosystem, we design other 3 sensitivity simulations as follows: Then, we will do ENSO composite for each sensitivity simulation and compare NEE , NPP, Rh anomalies.

18. NEE NPP Rh Trop: 1) NPP:Rh = 3:1 2) Prec:Ts = 1:1 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)

19. 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.

20. 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.

21. Reference • 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 Oscillation-induced 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