Variance and Vulnerability in Amazonian Forests:

1. Variance and Vulnerability in Amazonian Forests: Effects of climatic variability and extreme events on the structure and survival of tropical forests A synthesis study based on LBA science Steven C. Wofsy, (Presenting), Lucy Hutyra, Scott R. Saleska, J. W. Munger, Amy Rice, Greg Santoni, V.Y. Chow, Bruce C. Daube, John W. Budney, Alfram V. Bright, Harvard University; Michael M. Keller, Michael William Palace, Patrick Michael Crill, Hudson Silva, University of New Hampshire, Michael L. Goulden, Scott Miller, U. California, Irvine, Humberto Ribeiro da Rocha, USP, Plinio Barbosa de Camargo,Simone Aparecida Vieira, USP/CENA, Volker Kirchhoff, INPE, David Fitzjarrald, Ricardo Sakai, SUNY Albany, Osvaldo Luiz Leal de Moraes, UFFM LBA Science Meeting, Brasilia, July 2004.

2. STM: Eddy flux and Biometry C Balance Annual rates MgC/ha/yr loss to atmosphere Cumulative MgC ha-1 Bio- eddy metry flux uptake 2001 2002 2003 2004 (end: 11/04?)

3. Not all forests in the Amazon are equal Manaus has more biomass overall, in smaller trees, than Santarém and Rio Branco with longer dry seasons Biomass (Mg ha-1) Tree diameter class (cm) Vieira et al., 2004

4. 4 recruit 2 growth mortality 0 growth/loss rate (Mg C ha-1 yr-1) decomp-osition mortality -2 -4 -6 Carbon fluxes to live and dead biomass, 1999-2001 Small stems live wood change: +1.4  0.6 MgC ha-1 yr-1 dead wood change: -3.3  1.1 (loss) whole-forestnet:-1.9 1.0 (loss) Eddy Flux,u* cor-rected:-1.3 0.9(loss) net C loss |net C uptake Rice et al. 2004 & Saleska et al. 2003 Live Biomass(145-160 MgC/ha) Dead Wood(30–45 MgC/ha) Cf. Phillips et al. 2004

5. Why is FLONA Tapajós losing C (or in balance) • while recruiting and growing rapidly? • There are more large trees, faster turnover, and more recruitment and growth in smaller trees, than in comparable forests with shorter dry seasons. Dead wood stocks are notably large all over Tapajos, with different ages in different locations. Decay of dead wood nullifies growth. The forest looks in many ways like the 104 plots of Phillips, Malhi et al. • The Tapajos appears to be subject to frequent, relatively small scale disturbance. Disturbance is evidently a major factor in structuring the ecosystem. • Tapajos has a long dry season and is subject to sporadic droughts. Perhaps the disturbance is associated with drought or dry season severe storms.

6. Science questions: Why focus on variability and extreme events? • Climate and weather events represent a principal mechanism for disturbance of ecosystems, and disturbance is a major factor structuring ecosystems [Connell’s “Intermediate Disturbance” hypothesis, et seq.]. • Transitions to flammability in particular can cause dramatic shifts (degradation) of moist tropical forest systems [e.g. Nepstad et al. 2003]. • Despite their importance, extreme events (droughts) are rarely considered in vegetation change studies because variance is poorly known (data limitations) and poorly represented by atmospheric models . This is especially true for climate change simulations [e.g. Cox et al. 2001; Oyama & Nobre, 2003]. • Climate variations operate in concert with factors such as soils, land use, and hydrology, but at least conceptually climate effects can be studied independently. • How does climate variability affect Amazônian forests? • How might extreme climatic events control the structure of Amazonian forests, in particular, the transition between tropical forest and savanna?

7. Holdridge Life Zones and potential vegetation: the way most models deal with climatic effects on vegetation cover. Data courtesy of D. Skole drying Holdridge life zones (Holdridge 1967)

8. AmazonianVegetation: Multiple Equilibria, Persistence & Climate A After Wang & Eltahir 2000 B Another complication C Climate change shifts equilibria Vegetation, like climate, can have more than one state that is persistent and resilient, in analogy with movement of a ball on a landscape. Small disturbances lead to adjustments and return to the initial state. Large disturbances may cause the system to change to a new stable state, possibly to revert at a later time (cf. C. Nobre). A shift in climate, due to natural or anthropogenic causes, can change the landscape, as well as the frequency and magnitude of disturbance. The change in relative system stability might make a vegetation change irreversible (e.g. Cox et al, 2001), but it might take a disturbance for the shift to occur. Leads to the concept of instability. A complication: How does the system get to one or the other?

9. Our approach: Assess the influence of climatic variability and extreme events (droughts) on forests based on data for: climate, vegetation structure, vegetation cover, atmosphere-biosphere exchange. We will use a statistical simulation approach. This synthesis draws on key aspects of the pre-LBA and LBA data sets and science results. The issues raised by climate-vegetation feedback studies bring into sharp focus the importance of understanding major factors regulating vegetation change.

10. Amazon Precip Anomaly Figures from New, Hulme & Jones, J. Climate. 2000 Mm/yr -600 0 600 1900 2000 • New et al. (2000, CRU/East Anglia): • Separated global station data into mean and anomaly (deviance) fields, • Interpolated and combined them. • Product: global monthly precipitation for 1900-1995, gridded (0.5o x 0.5o) Density of reporting stations (0.5o grid)

11. 400 Mean = 2091 mm 200 0 J J J D 400 Mean = 2279 mm 200 0 J J J D 400 Mean = 2020 mm 400 Mean = 1373 mm 200 200 0 0 J J J D J J J D Lat Lon Precipitation data from: New et al. 2000

12. From Eddy flux data: Shuttleworth (ABRACOS); da Rocha et al. & Saleska et al. (LBA); 130 115 0 100 200 Evap. (FPET mm/month) 0 100 200 300 Precipitation (mm/month) Net evaporation = FPET - precipitation Forest Potential Evapotranspiration = ET if a forest were present at all pixels. From CRU [New et al.] 95 year time series of Net Evap, gridded 0.5x0.5 degrees. Simulate a 2500 year time series of Net Evaporation, using observed deviances and autocorrelation. Drought yr = 12 months with Net Evap > 0.

13. Auto-regressive fit to monthly deviances Simulate Net Evaporation = (FPET –Precip) means & variance Deseasonalized & detrended Autocorrelation Examine spatial distribution of droughts: frequency and intensity. Compare extreme dry events to Skole’s 1980 vegetation map Lag (years) Amazon precipitation data New et al. (2000) Summary of conceptual framework

14. SANTAREM TAP. -2.4 -54.3 63yrs MANAUS -3.1 -60 93yrs CUIABA -15.6 -56.1 102yrs 1.0 Auto Correlation Coef. 0.8 0.6 0.4 0.2 0.0 0.0 0.5 1.0 1.5 2.0 0.0 0.5 1.0 1.5 2.0 2.5 0.0 0.5 1.0 1.5 2.0 2.5 CRU, Lat= -3.25 , lon= -60.25 CRU, Lat= -2.25 , lon= -54.25 CRU, Lat= -15.75 , lon= -56.25 1.0 0.8 0.6 0.4 95% signif. 0.2 0.0 0.0 0.5 1.0 1.5 2.0 2.5 0.0 0.5 1.0 1.5 2.0 2.5 0.0 0.5 1.0 1.5 2.0 2.5 Lag (years) Are the statistics of the CRU Precip data reliable?Autocorrelation of precipitation time series: Original station data (upper), New et al. reconstruction (CRU, lower)

15. Total number of years of drought (2500 year simulation) 0.3 3 33 per century

16. Water deficit from 50 year drought‡ event Latitude (m H2O) Longitude ‡Deficit in the last year of a multi-year drought (50yr return interval in a 2500 yr simulation)

17. Dense tropical forest Open tropical forest Savanna Deciduous Forest The 1980 Amazônian vegetation distribution agrees well with the distribution of vegetation types. Cut 1 Cut 2 50 yr drought=0.1 m water Vegetation distribution data: D. Skole

18. Cut 1, -53.2 W 2.0 12 1500 2000 2500 8 1.0 4 0 0.0 -15 -10 -5 0 Lat Mean Annual Precipitation (mm) 50 year drought deficit (m H2O) Number of droughts per 100 yrs Cut 2, –8.3 S 2.0 12 1500 2000 2500 8 1.0 4 0 0.0 -70 -65 -60 -55 -40 -45 Longitude key value: 1—3 big droughts/100 yr

19. -25% -10% current Projections of regions converted to savanna for 10 and 25% reduction in precip. Note that this approach captures the effects of rainfall patterns (e.g. sea breeze front).

20. The variability of climate changes with time and can have important ecological impacts. Since variability is a second-order quantity and extreme events are rare, it is very difficult to assess the role of variability on ecosystems.

21. Summary and conclusions • Disturbance is a major factor in structuring the primary forest at STM, likely others. • The CRU reconstructions provide the basis for assessing the intensity and recurrence times for extreme droughts, a major mechanism for disturbance. • We simulate long time series of net precipitation using an autoregressive model. As mean precipitation approaches a critical value (1600 mm/yr; dry season > 5 months), severe droughts (>2 consecutive years net positive evaporation) recur at 25-100 year intervals. This appears to be the threshold for replacement of tropical forests by savanna or woodland vegetation. Soils and topography are other major factors. • The transition to savanna likely requires forests to ignite, and the presence of flammable savannas (or farmers) nearby are an important additional risk factor. • A sizable fraction of Amazônian forests appear vulnerable to reduced precipitation, higher T increasing evaporation, or increased variance of rainfall. Data sets/concepts used: CRU precip; Fizjarrald/NCAR climate data; Nepstad precip and flammability; Shuttleworth; da Rocha/Goulden; Saleska Eddy FPET; Skole 1970s veg.; Phillips/Malhi/Vieira/Camargo tree mortality & size dist.; Chambers and our own CWD; Nobre/Avissar/Eltahir multiple states. Lucy, Scott, Steve, offer thanks to all! We have enjoyed LBA “beyond earth and sky”. Long term data needed: phenology, rainfall (MODIS, TRMM, stations).

22. Having fire-prone woodlands at your back is a factor too (extreme event gradient is steep!)

26. Multiple equilibria: coupled climate and vegetation (Oyama & Nobre 2003) ForestCerradoDesert Amazon soils map and potential flammability (Nepstad et al. 2004) Before deforestation After deforestation Potential Vegetation Does climate variability play the key role linking together climate change, edaphic factors, and human use factors?

27. Floresta densa tropical savana Water loss from 50 yr drought (m) Tail of the distribution Quantiles of std. normal semidecidual tropical Floresta aberta tropical Use Missy’s suggestion, add Skole’s map and a ref.

28. These are for ~km 67 using the cru data. Raw monthly net evaporation Deseasonalized & Detrended Lag (yrs) Lag (yrs)

29. Wet season MODIS March 22, 2001 Dry season August 12, 2001

30. STM: relationship between annual precipitation and NEE Dry 2002-wet 2003 2 Dry 2001-wet 2002 1 NEE, MgC ha-1 yr-1 Losses of CWD? 0 -1 Dry 2003-wet 2004 -2 1500 1600 1700 1800 1900 2000 2100 annual precipitation(mm; Nepstad data)

31. 40 Recruitment 30 Net 20 10 density change, stems ha-1 0 Mortality -10 Outgrowth -20 10 35 60 85 110 135 160 185 size class, cm DBH Consistent with the results in Phillips et al. 2004, we observed also observed high rates of stem recruitment. However, our high recruitment was coupled with large stocks of coarse woody debris. Rice et al. 2004

32. Relationship between Evaporation and precipitation from Eddy Flux data (km 67, STM) 120 120 100 100 Mean monthly water flux, mm/month 80 80 60 60 0 0 100 100 200 200 300 300 Total monthly precip (mm) [Nepstad et al.]