Proposed rough outline for a cross-cutting themed uncertainty paper
NOTE: Much of the proposed content is in prep/in review, so important to get anticipated publication dates from some of the lead authors
Abstract: Quantifying uncertainty as to the future state of ecosystems in global change research has typically focused on one process: emissions, climate response, veg response, or their feedbacks. An exception to this is model simulations in which each of the uncertainty components is quantified via a fully factorial set of drivers and models. However, such an approach typically focuses on the modeled spread (‘model democracy’ view) as a means for a quantification of uncertainty. Here we adopt a different approach to highlight and review how, in Amazonia, recent model evaluation and/or benchmarking activities across all processes (both climate and vegetation responses) provide important critical constraints on the range of uncertainty regarding ‘Amazon dieback’. A theme which emerges is that the ‘model democracy’ view often either under- or over-estimates the range of possible outcomes.
We stress the importance of measurement campaigns as well as model-data intercomparisons to provide important insights as to the range of potential outcomes. Importantly, we highlight how observations challenge the paradigm of ‘Amazon dieback’ as a binary outcome, arguing for a continuum of alternate stable states, among which full dieback remains an extreme end point of several possibilities.
Outline of figures
Figure 1. Conceptual diagram of uncertainty showing the cascading/factorial increase in uncertainty starting with emissions scenarios, global climate response, interactions of regional Amazonia climate with global-scale motion, vegetation response, and vegetation feedbacks.
Figure 2. Emissions scenario uncertainty (e.g., SRES or RCP scenarios) Possibly showing emissions uncertainties and how they translate into climate response (something showing the range of both emissions possibilities as well as the range of possible climate responses). This is two elements of uncertainty (emissions and climate response) – showing possible spread in a metric such as precip or dry season length
Figure 3. Constraining the future precipitation regime of Amazonia with constraints on large-scale atmospheric motion (Juan-Pablo figure).
Figure 4. Uncertainty in land use change & hydrology
Figure 5a,b,c,d. Parallel uncertainties in the vegetation short-term (photosynthesis, respiration, NPP) response to both temperature (Doughty 2012, Doughty et al. 2008, Rowland et al. in prep, Kruijt et al. in prep), moisture (Powell et al., 2013; Christoffersen et al. in prep)
Figure 6. Longer-term uncertainties in the mortality response of vegetation (Powell et al. 2013,
Galbraith et al. in prep, plus updates from Caxiuana drought experiment in its 12 th yr) – uncertainty as to both the driver (best-yet is MCWD) and process (hydraulic failure / carbon starvation / temperature effects)
Figure 7. Uncertainty in spatial variability of biomass/NPP across the Amazon basin (M. Johnson, T.
Baker et al., in prep) – implications for the response to CO2 in the models (progressive nutrient limitiations).
Figure 8. Something regarding the range of possible sensitivities of climate to vegetation dieback

Figures
Figure 3. Empirical constraint on future changes in the seasonal precipitation distribution in
Amazonia. From Figure 3 of Bosier et al. in prep.

Figure 5x. Empirical constraint demonstrating of lack of moisture sensitivity in models. From
Figure 1 of Christoffersen et al. (in prep). Modeled and observed ecosystem-level water fluxes at
Caxiuana, Brazil under normal conditions (cax.d00; left panel) and drought (cax.d50; right panel). a)
Observed mean seasonal cycle of precipitation (P) (bars) and net radiation (R n
) (lines) expressed in mm month -1 water equivalent, +/- 1 s.d. b) Observed +/- 1 s.d (points & error bars) and modeled (lines) transpiration (T) (mm d -1 ). Observed stand-scaled sap flux rate from Fisher et al., 2007.

Figure 5xx.Empirical constraint demonstrating lack of moisture sensitivity on models with
respect to mortality (via plots of biomass).From Figure 1 of Powell et al. (2013). Annual aboveground biomass (AGB; kg C m-2) predicted for Tapajos (TNF; left side) and Caxiuana (CAX; right side) National Forests. Colored lines are individual model predictions and the black line is the five-model ensemble mean. The shaded area is the 95% confidence interval (CI) of the models. Open symbols are published observations (mean +/- 95% CI (when reported); TNF: Brando et al., 2008; CAX: da Costa et
al., 2010). d0 and d50 are drought levels indicating a 0 and 50% reduction in precipitation, respectively.
The ∆ plots (c, d) show the amount the control (a, b) AGB was altered by the 50% drought treatment.
Updatable w/ Galbraith et al. unpublished.

Figure 7. Empirical constraint on the spatial variability of biomass (NOTE: a placeholder – to be updated w/ M. Johnson’s maps)