Variance and Vulnerability in Amazonian Forests: Effects of climatic variability and extreme events on the structure and survival of tropical forests 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 A synthesis study based on LBA science

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

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

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

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.

Science questions: Climate variations operate in concert with factors such as soils, land use, and hydrology, but at least conceptually climate effects can be studied independently. 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]. 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 ?

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

AmazonianVegetation: Multiple Equilibria, Persistence & Climate After Wang & Eltahir 2000 B A 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 complication: How does the system get to one or the other? C Climate change shifts equilibria 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. Another complication

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.

Amazon Precip Anomaly Mm/yr 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 , gridded (0.5 o x 0.5 o ) Density of reporting stations (0.5 o grid) Figures from New, Hulme & Jones, J. Climate. 2000

JJJD Mean = 2020 mm JJJD Mean = 2091 mm JJJD Mean = 2279 mm JJJD Mean = 1373 mm Precipitation data from: New et al Lon Lat

Precipitation (mm/month) Evap. (FPET mm/month) From Eddy flux data: Shuttleworth (ABRACOS); da Rocha et al. & Saleska et al. (LBA); Simulate a 2500 year time series of Net Evaporation, using observed deviances and autocorrelation. Drought yr = 12 months with Net Evap > 0. 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.

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

Are the statistics of the CRU Precip data reliable? Autocorrelation of precipitation time series: Original station data (upper), New et al. reconstruction (CRU, lower)

Total number of years of drought (2500 year simulation) per century

Longitude Latitude (m H 2 O) ‡ Deficit in the last year of a multi-year drought (50yr return interval in a 2500 yr simulation) Water deficit from 50 year drought ‡ event

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

year drought deficit (m H 2 O) Number of droughts per 100 yrs key value: 1—3 big droughts/100 yr Cut 1, W Longitude Cut 2, –8.3 S Lat Mean Annual Precipitation (mm)

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). -25% -10% current

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.

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 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).

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

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

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

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

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

annual precipitation(mm; Nepstad data) NEE, MgC ha -1 yr Dry 2002-wet 2003 Dry 2001-wet 2002 Dry 2003-wet 2004 STM: relationship between annual precipitation and NEE Losses of CWD?

Rice et al 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.

Total monthly precip (mm) [Nepstad et al.] Mean monthly water flux, mm/month Relationship between Evaporation and precipitation from Eddy Flux data (km 67, STM)