1. Seasonality & Interannual variability in photosynthetic metabolism of Amazon rainforests: insights from remote sensing Kamel Didan, Scott Saleska, Natalia Restrepo-Coupe, Alfredo Huete

2. Seasonality & Interannual variability in photosynthetic metabolism of Amazon rainforests: insights from remote sensing Kamel Didan, Scott Saleska, Natalia Restrepo-Coupe, Alfredo Huete

3. What controls the seasonality of photosynthesis across the Amazon basin?: A cross-site analysis of eddy flux tower measurements from the Brasil Flux network Natalia Restrepo-Coupe, Scott R. Saleska, Humberto R. da Rocha, Bart Kruijt, Antonio D. Nobre, and Renata G. Aguiar, Alessandro C. da Araujo, Laura S. Borma, Osvaldo M. R. Cabral, Plinio B. de Camargo, Fernando L. Cardoso, Antonio C. Lola da Costa, David R. Fitzjarrald, Michael L. Goulden, Lucy R. Hutyra, Jair M. F. Maia, Yadvinder S. Malhi, Antonio O. Manzi, Scott D. Miller, Celso von Randow, Leonardo D. da Abreu Sá, Ricardo K. Sakai, Julio Tota, Steven C. Wofsy, Fabricio B. Zanchi

4. Motivation: What is the seasonality of ecosystem metabolism? – Early results from Tapajos National Forest (K67 site) showed unexpected seasonality A B NEE (flux to atmosphere) kg C ha -1 month -1 R tot ▲ GPP ▼ TEM ○ ○ Data IBIS X X uptake loss to atmosphere GPP or R tot, kg C ha -1 month -1 Saleska et al. (2003) Science. Observations Models JanFebMarAprMayJunJulAugSepOctNovDec Dry Season Composite annual cycle, 2001-2003

5. Motivation: What is the seasonality of ecosystem metabolism? – Early results from Tapajos National Forest showed unexpected seasonality – focus on photosynthesis JanAprJulOct 500 1000 1 2 3 Models Data GPP (MgC ha -1 mo -1 ) PAR (  mol m -2 s -1 ) Dry Season Is this typical of sites across the Amazon? If not, what are the differences? What mechanisms control seasonal variation in ecosystem GPP? How do Amazonian ecosystems allocate carbon seasonally? (= Gross Primary Production, GPP) Questions:

6. BrasilFlux Sites - annual rainfall Forest: K34 (Manaus) K67, K83 (Santarem) CAX (Caxiuana) RJA (Res. Jaru) JAV (Bananal) Pasture/Ag: K77 (Santarem) FNS (Ji Parana) Cerrado PDG (Sao Paulo)

7. BrasilFlux Sites – Central Amazon Forest: K34 (Manaus) K67, K83 (Santarem) CAX (Caxiuana) RJA (Res. Jaru) JAV (Bananal) Pasture/Ag: K77 (Santarem) FNS (Ji Parana) Cerrado PDG (Sao Paulo)

8. BrasilFlux Sites – Southern Amazon Forest: K34 (Manaus) K67, K83 (Santarem) CAX (Caxiuana) RJA (Res. Jaru) JAV (Bananal) Pasture/Ag: K77 (Santarem) FNS (Ji Parana) Cerrado PDG (Sao Paulo)

9. BrasilFlux Sites – Southern Savanna Forest: K34 (Manaus) K67, K83 (Santarem) CAX (Caxiuana) RJA (Res. Jaru) JAV (Bananal) Pasture/Ag: K77 (Santarem) FNS (Ji Parana) Cerrado PDG (Sao Paulo)

10. BrasilFlux Sites – Raw data series Pe-de-Gigante (PDG) Manaus (K34)

11. Gross Ecosystem Productivity, GEP Ecosystem Respiration, Re NEE = eddy-flux + change in canopy storage (when missing, canopy storage is filled) BrasilFlux Sites and Methods

12. Gross Ecosystem Productivity, GEP Ecosystem Respiration, Re NEE = eddy-flux + change in canopy storage (when missing, canopy storage is filled) BrasilFlux Sites and Methods GPP (g C m -2 day -1 ) PAR (umol m -2 sec -1 ) Define: Photosynthetic Capacity (Pc) = GPP at PAR: 700 -900

14. Results: Seasonal GPP and Pc Central Amazon Forests Forest: K34 (Manaus) K67 (Santarem) CAX (Caxiuana) RJA (Res. Jaru) JAV (Bananal) Pasture/Ag: K77 (Santarem) FNS (Ji Parana) Cerrado PDG (Sao Paulo)

15. Results: Seasonal GPP and Pc Central Amazon Forests Forest: K34 (Manaus) K67 (Santarem) CAX (Caxiuana) RJA (Res. Jaru) JAV (Bananal) Pasture/Ag: K77 (Santarem) FNS (Ji Parana) Cerrado PDG (Sao Paulo)

16. Results: Seasonal GPP and Pc Central Amazon Forests Forest: K34 (Manaus) K67 (Santarem) CAX (Caxiuana) RJA (Res. Jaru) JAV (Bananal) Pasture/Ag: K77 (Santarem) FNS (Ji Parana) Cerrado PDG (Sao Paulo) All sites maintain high or increasing dry season GPP  No evidence of seasonal water limitation

17. Results: Seasonal GPP and Pc Central Amazon: Forest vs. Agriculture Forest: K34 (Manaus) K67 (Santarem) CAX (Caxiuana) RJA (Res. Jaru) JAV (Bananal) Pasture/Ag: K77 (Santarem) FNS (Ji Parana) Cerrado PDG (Sao Paulo)

18. Results: Seasonal GPP and Pc Southern Amazon Forests Forest: K34 (Manaus) K67 (Santarem) CAX (Caxiuana) RJA (Res. Jaru) JAV (Bananal) Pasture/Ag: K77 (Santarem) FNS (Ji Parana) Cerrado PDG (Sao Paulo)

19. Results: Seasonal GPP and Pc Southern Amazon Forests and Pasture Forest: K34 (Manaus) K67 (Santarem) CAX (Caxiuana) RJA (Res. Jaru) JAV (Bananal) Pasture/Ag: K77 (Santarem) FNS (Ji Parana) Cerrado PDG (Sao Paulo) PastureForest

20. Results: Seasonal GPP and Pc Forest: K34 (Manaus) K67 (Santarem) CAX (Caxiuana) RJA (Res. Jaru) JAV (Bananal) Pasture/Ag: K77 (Santarem) FNS (Ji Parana) Cerrado PDG (Sao Paulo) Central versus Southern Amazon Forests

21. Results: Seasonal GPP and Pc Southern Savanna

22. Results: Seasonal GPP and Pc

23. Results: GPP, Pc Seasonality, relative to start-date

25. Results: Seasonal Climate, Central Amazon Radiation: Low variability in top-of-atmosphere radiation, + Surface PAR controlled by clouds

26. Results: Seasonal Climate, Southern Amazon Radiation: Higher variability in top- of- atmosphere Radiation (with latitude), + Shift in timing of dry season:  TOA solar minimum corresponds to dry season  little seasonal variation in surface radiation

27. Results: Seasonal Climate, Southern Savanna PAR sunlight: Even higher variability in top- of-atmosphere Radiation (with latitude):  TOA solar minimum corresponds to dry season  dry-season dip in surface radiation

28. Results: GPP Mechanisms: PAR (not!)

29. Contrast predictability GEP vs. ET Results: GPP Mechanisms

30. How much capacity is ‘spent’ on growth (GEP=  PAR Pc) GPP Mechanisms: fraction of Photosyn- thetic capacity used is predicted by PAR

31. How much capacity is ‘spent’ on growth (GEP=  PAR Pc) GPP Mechanisms: fraction of Photosyn- thetic capacity used is predicted by PAR Except pasture/ agriculture!

32. - PAR doesn’t control monthly photosynthesis directly. - PAR influences the fraction of capacity utilized as GPP What controls the seasonality of photosynthetic capacity?

33. where: P c : Ecosystem photosynthetic capacity (gC m -2 d -1 ) Litter fall, leaf flush(gC m -2 d -1 ) Leaf-level parameters A: Photosynthetic assimilation per area of leaf (gC m -2 d -1 ) SLA: specific leaf area (m 2 gC -1 ) BrasilFlux Sites and Methods Seasonality of leaf -growth

34. Based on EC measurements Field measurement Dry and wet season field measurement where: P c : Ecosystem photosynthetic capacity (gC m -2 d -1 ) Litter fall, leaf flush(gC m -2 d -1 ) Leaf-level parameters A: Photosynthetic assimilation per area of leaf (gC m -2 d -1 ) SLA: specific leaf area (m 2 gC -1 )

35. Results: GPP Mechanisms – K67(Tapajos) leaf-flush leaf-fall wood increment (gC m -2 d -1 ) 0 1 2 3 4

36. leaf-flush leaf-fall wood increment (gC m -2 d -1 ) 0.3 0.4 0.5 0 1 2 3 4 Results: GPP Mechanisms – K67(Tapajos)

37. Results: GPP Mechanisms – 3 forests Seasonality of photosynthetic capacity determined by leaf phenology. Leaves grow in the dry season when the sun shines (in central Amazon)

38. Central Amazon Forest Sites  Photosynthesis shows little evidence of seasonal water limitation  GPP is high -- or even increasing -- as the dry season progresses. Southern forest site (Jarú), the converted sites (Santarém K77, and Ji-Paraná FNS), and the savanna site (PDG)  Seasonal patterns consistent with varying degrees of water stress  All show dry-season declines in GEP. Leaf-flush model indicates dry season forest green up at central Amazon forest sites Complementary patterns in the timing of allocation in central Amazon (wood grows in wet season, leaves flush in the dry season ) Conclusions

39. Acknowledgments Funded by the National Aeronautics and Space Administration (NASA) LBA

41. 024 0 1 2 3 4 y=-1.46x+2.70 R 2 =0.50 p=0.000 K67 leaf-flush (gC m - 2 d - 1 ) wood-increment (gC m -2 d -1 ) 0.20.30.40.5 0 1 2 3 y=7.02x+-1.27 R 2 =0.27 p=0.000 K67 leaf-flush (gC m - 2 d - 1 ) PAR (mmol m -2 s -1 ) 0.30.40.5 0 1 2 3 4 y=-9.71x+4.85 R 2 =0.78 p=0.000 K67 leaf-flush (gC m - 2 d - 1 ) 0-40cm (m 2 m -2 ) Results: GPP Mechanisms – K67(Tapajos) Soil moisture

42. At all Amazonian sites  Bi-weekly Pc declines with increasing light, contradicting model assumptions that assume days with high light as more productive than days with low light. ET and photosynthesis are often thought of as coupled process.  Net radiation (Rn) controls ET (R>.30)  GEP respond differently to Rn  GEP seems to be controlled by complex patterns of production and loss of photosynthetic capacity  The fraction of capacity utilized as GPP, depends on light levels (GPP Pc- vs. PAR) Results: GEP Environmental Controls

46. Leaf-flush model additional data

49. Relation GEP and NEE and air temperature

50. No ta control High par high GEP

54. 2005 Drought

60. BrasilFlux Tower Sites: GEP calculations Missing Sco

61. BrasilFlux Tower Sites: GEP calculations Missing Sco 2