Prepare for leaf senescence Peak litterfall Flush of leaves Max aerosol load μmol m -2 s -1 Climatic variability, carbon exchange and vegetation vulnerability in Amazonia Lucy R. Hutyra 1, J.William Munger 1, Scott R. Saleska 2, Plinio B. de Camargo 3, Steven C. Wofsy 1 1 Dept. of Earth & Planetary Sciences, Harvard University; 2 University of Arizona; 3 CENA/USP, Piracicaba, SP Seasonality of Carbon and Water exchange in Amazonia Figure 4. We measured CO 2 and H 2 O fluxes and profile concentration a 64 m tall eddy flux tower. Climatic variability and vegetation vulnerability in Amazonia [Hutyra et. al., 2005] X SCIENTIFIC GOAL We assessed the vulnerability and resilience of Amazonian vegetation to climate change by analyzing observed climate-vegetation relationships in a statistical framework using climate data, observed vegetation distributions, evapotranspiration rates based on eddy flux data (ET), and water balances. Figure 3. (a) Observed drought frequency (% years); (b) distribution of savanna, transitional vegetation, and forest across the legal Amazon; (c) land area (km 2 ) of vegetation types with given drought frequency (%), forest land area is multiplied by 0.1 for scaling. Figure 1. (a) Observed and modeled forest ET for the Santarem study site, R 2 = 0.68; (b) time series for measured forest ET, the FET model (1), and potential evapotranspiration (PET) [Thornwaite, 1948]. METHODS Data for water fluxes and temperature, from January 2002 through November 2004, were combined to develop a model of actual ET for evergreen Amazonian tropical forest, denoted forest evapotranspiration (FET): FET (mm day -1 ) = *T (1) where T is monthly mean temperature (C). When fit to 38 months of environmental measurements, equation (1) explained 68%of the total variance (Figure 1). We used the Climate Research Unit’s (CRU) 100-year gridded (0.5 o x 0.5 o ) time series for temperature and precipitation [Mitchell et al., 2003] to model the FET across the Amazon (Figure 2). To derive a measure of drought occurrence, we computed the quantity of soil water available to trees (Plant Available Water, or PAW; units: mm H 2 O), PAW i = PAW i-1 + P i - FET i (2) where i indexes the month of the 100 year record. Values exceeding PAWmax were assumed lost as runoff. The spatial distribution of PAWmax was adapted from Kleidon [2004], who applied inverse methods to a land surface model optimizing photosynthesis. This PAWmax applies to current vegetation assemblages, under current climate. A drought was assessed at any grid cell where PAW declined to less than 75% of PAWmax for 5 or more months in a year, implying a dry period exceeding 6 or 7 months (Figure 3a). The spatial distribution of estimated drought frequencies in 100 years was compared with vegetation in the legal Brazilian Amazon, classified using Landsat data from the early 1980s (prior to most forest clearing, figure 3b). RESULTS Our values for drought frequency (Figure 3a) are highest along the southern and eastern edges of the legal Amazon, but less frequent droughts occurred in the central basin. Areas with high drought frequency are associated with regional precipitation minima and/or high temperature variability. The current distribution of vegetation (Figure 3b) strikingly follows drought frequency, with savanna replacing forest and transitional vegetation as drought frequencies increase (Figure 3c). Our study supports the view that forests in areas of high drought frequency (>45% drought probability) could shift to transition forests or savanna, if aridity increases as predicted by climate change models [Cox et al., 2004]. Potentially at risk are over 600,000 km2 of forest (Figure 3), >11% of the total area. Our maps show that increased aridity may lead to bisection of Amazonian equatorial forests. IMPLICATIONS/CONCLUSIONS Forest areas with high climate variability are vulnerable to loss of forest with either increased mean temperature, or increased variability in temperature and/or precipitation. Our analysis provides a physical quantity (PAW deficit) to predict vegetation type indicating that the seasonality of soil moisture is a critical factor determining forest-savanna boundaries. The critical links between fire, climate, and land use are highly uncertain in current coupled climate-vegetation models. In order to assess vegetation vulnerability to climate change, models must capture variability of climate, the non-linear, hysteretic behavior of vegetation response to rising drought frequency, the synergistic effect of forest fragmentation and development, and the occurrence of landscape-changing fires. SCIENTIFIC GOAL The stability of Amazonian equatorial forests, and the fate of their immense stores of organic carbon, depend on the ecosystem response to climate and weather. This study presents 4+ years of eddy covariance measurements of carbon and water fluxes and their response to environmental conditions in an Amazonian old-growth tropical forest. METHODS The study site is located in the Tapajós National Forest, Para, Brazil (TNF, Figures 4). Eddy-flux measurements of CO 2 and H 2 O were made at a height of 57.8 m using a sonic anemometer (CSAT-3) and a closed-path infrared gas analyzer (LI-6262). Net ecosystem exchange of CO 2 (NEE) was calculated as the sum of CO 2 flux above the canopy and CO 2 storage flux. Ecosystem respiration (R) was determined based on nighttime NEE measurements during well-mixed periods. Gross ecosystem exchange (GEE) was calculated as the difference between NEE and R during daytime periods. CONSTRAINING ECOSYSTEM RESPIRATION ESTIMATES Biases in the day/night measurements of CO 2 flux can affect estimates of carbon exchange due to the prevalence of weak vertical mixing during the nighttime hours. To constrain our R estimates we used three independent approaches: (a) u* filtering to correct for underestimation of nighttime fluxes; (b) seasonal light response relationships between PAR and NEE; (c) Radon-222 (Rn) derived nighttime NEE estimates. We expected that the respiration should be largely independent of the turbulence, nevertheless, measured NEE decreased in calm conditions suggesting that there was ‘lost flux’. Approximately 57% of the nighttime hours at this site were calm, with u*<0.22 m s -1. We corrected for lost flux by filtering calm night periods and replacing the data with the mean value of nearby well mixed time periods. We examined the NEE-light relationship (Figure 5) using a nonlinear least squares approximation (hyperbolic function) (3) fitted NEE and PAR binned by 1 μmol m -2 s -1. The intercept, a1, of this overall model provides an independent estimate of the mean ecosystem R and agrees very well with the mean nighttime, u* filtered, NEE measurements, 9.38 and 8.58 μmol m -2 s -1, respectively. Martins et al. [2004] independently assess raw and u* corrected NEE measurements by comparing them to CO 2 exchange derived from Rn canopy concentration, Rn soil flux measurement, and profile Rn concentrations. Rn-derived nighttime NEE was found to be 9.00±0.99 μmol m -2 s -1 for the wet season (June-July 2001) and 6.39±0.59 μmol m -2 s -1 in the dry season (November- December 2001) and agrees very well with u* filtered NEE measurements during the same period (8.65±1.07 and 6.56±0.73, respectively) [Martins et al., 2004]. Figure 5. NEE as a function of PAR,. The vertical line denotes 0 μmol m-2 s-1 PAR. The horizontal line is the mean nighttime NEE (8.58 μmol m-2 s-1, u*≥0.22). IMPLICATIONS/CONCLUSIONS Contrary to expectations, this forest does not show signs of seasonal water limitation on growth despite a 5-month dry season. CO 2 uptake responds primarily to light on hourly time scales, but photosynthesis overall maximizes in the middle of the dry season, responding to ecophysiological (flushing of new leaves) and atmospheric (high aerosol loading) changes. Leaf phenology is the major control on photosynthesis, but EVI lags the phenological response by 2 months and explains only 41% of the observed monthly variance. Annual carbon balance was very sensitive to weather anomalies, particularly the timing of the dry-to-wet seasonal transition, with mean net loss of 939 kg C ha -1 yr -1 (observed range of -221 (uptake) to 2677 (loss) kg C ha -1 yr -1 ). The climatic sensitivity has significant implications for Amazonian carbon balances on annual to decadal time scales Precip (mm 5-day -1 ) Rnet (W m -2 ) LE (W m -2 ) H (W m -2 ) DOY since 2000 Figure 7. 5-day mean time series for (a) latent heat flux;(b) sensible heat flux ;(c) net radiation; (d) precipitation [dry season indicated by blue shading]. The annual mean fraction of water lost through LE and the precipitation inputs was approximately 0.53 (1116mm/2111mm), 0.64 (1114/1740), 0.49 (1137/2311), 0.51 (1123/2201) for , respectively. The dry season LE was insensitive to dry season precipitation and nearly constant across years even as dry season precipitation varied by 40%. During the dry season the ratios of evaporation to precipitation were 1.81 (503mm/279mm), 1.16 (522/448), 1.28 (514/402), 1.40 (536/383), respectively. SEASONAL CONTROLS ON LATENT HEAT, GEE, R, AND NEE Peak litterfall rates were observed August and September [Rice et al., 2004] and leaf flush across the Basin occurs in (August-October) [Figure 6, Huete et al., 2006]. To quantity the phenology effects on GEE, we calculated the mean monthly GEE at a fixed light level, 800 ± 75 μmol m-2 s-1, and compared the time series with that of leaf litterfall and the remotely sensed vegetation greenness parameter EVI at the TNF [Huete et al., 2006]. Leaf litterfall rates explained 76% of the observed variance in monthly GEE. EVI also correlated with GEE, explaining 41% of the observed variance, when lagged by 2 months. The lagged correlation in EVI is not surprising since it takes time for the leaves to fully elongate. The mean GEE, across all light levels, also correlated well with litterfall and EVI, explaining about 40% of the observed variance, but by looking at a narrower light window we can remove the variance due to seasonal difference in sunlight. This forest does not show signs of water limitation on growth (Figure 8) On short (hourly to daily) time scales, there was no significant relation between temperature and ecosystem respiration. On monthly time scales, respiration could be well predicted by temperature and precipitation patterns (figure 9) Annual carbon balance, particularly ecosystem respiration, is very sensitive to weather conditions during the dry to wet seasonal transition (Figures 8,9) Figure 8. The annual carbon balance at km 67 has shown a mean net loss of 939 kg C ha -1 yr -1 (observed range of -221 (uptake) to 2677 (loss) kg C ha -1 yr -1 ). During the wet season and early dry season, R dominates GEE and the ecosystem is a net carbon source. The dominance switches by September when R becomes moisture limited. Overall, GEE maximizes in the middle of the dry season. There may be a trend of increasing carbon uptake. Annual ecosystem C losses were decreasing between , but a weather anomaly in late 2005 resulted in net carbon loss. Cumulative NEE (Kg C ha -1 yr -1 ) DOY Figure 9. Time series for 4+ years of monthly mean GEE and R. There is a drop off in GEE around May, before the start of the dry season, as the ecosystem begins leaf senescence. Leaf litterfall peaks in Aug. and Sept. when mean GEE is at its minimum. The new flush of leafs begins to emerge around September at the same time as GEE rates begin to increase. High aerosol levels increase the diffuse light and photosynthetic efficiency, aerosol levels are highest between September and October. Respiration decreases in the dry season due to moisture limitation and shows the greatest variability at the dry-to-wet seasonal transition. Figure 6. Monthly mean GEE at PAR of 800 ± 75 μmol m-2 s-1, open circles. Monthly mean leaf litterfall rate, July 2000 – May 2005, closed circles. Monthly mean Enhanced Vegetation Index (EVI), , triangles [ Huete et al. 2006] Longitude Latitude X Land area (100 km 2 ) Frequency of drought (%) Figure 2. Mean annual modeled FET (mm day -1 ) from equation (1) Longitude Latitude Model FET (mm/day) Measured ET (mm/day) Month ET (mm/day)