2. Early warning System for critical transitions in Amazonia : Framework

3. Coupled Atmosphere-Land surface Analyse and new ESM results DGVMs test urgent issues rivers Coupled LUC People Scenario analysis Policy analysis Land-use change model Blue-print Early-Warning System Critical Ecosystem Services

4. Develop a proposal (‘Blue-print’) for an early warning system for imminent tipping points

5. Early Warning System What to warn about? – Not only critical transitions, but also gradual change? – Which changes are the biggest concerns of stakeholders? – Degradation of ecosystem services in what time scales (years – decades) ? – How do we translate large-scale change into local impact? – Are there likely policy options in response?

6. Amazonia maintains its own regional water cycle

7. Amazonia stores CO 2 in biomass On area basis not extremely high, but there is so much of it: total 86 Pg C Globally in atmosphere: 760 Pg

8. High(est?) Biodiversity

9. People!

10. What is needed to preserve ecosystem services? Does most of it come down to preserving forests?

11. How to define a threshold/change Magnitude of impact Time and spatial scale Threshold in impact or in forcing?

12. How to define thresholds Good et al, 2012

13. Dry season length Temperature Forest sustainable Forest unsustainable What kind of thresholds can we warn for? ‘Regional forcings threshold’ + Emissions set to zero here Committed climate change after emissions are stopped (lagged ocean heat uptake) IMPACT threshold: Critical change occurs REGIONAL threshold: Critical change is unavoidable MITIGATION threshold: Existing policies are not sufficient any more to avoid critical change Good et al, in prep

14. Indicators What would change look like – models, proxy observations How would the ecosystem approach the change – Variability indicators? Examples... – Mitigation threshold: model forecasts of future T, seasonality, land-use – Regional threshold: monitoring and model T, Precip, land-use – Impact threshold: monitoring NPP, soil carbon, flammability, hydrology

15. Forecast climate-induced changes to the Amazon till 2100 ‘ 40% deforestation and/or 3 degrees warming leads to savannisation’ (Nobre et al., various)

16. Precipitation change No fire and no LU + fire and LU RCP4.5 - 2050

17. Dry season length RCP4.5 - 2050 No fire and no LU + fire and LU

18. Biomass RCP4.5 - 2050 No fire and no LU + fire and LU Sampaio et al, in prep

19. Tropical forest Savanna state triggered by climate change or deforestation Stability of savanna enhanced by increased droughts and fires Tipping points in Amazonia Cardoso and Borma, 2010 Environmental variation Stability

20. Analysing tipping points with simple models Example: Simple Amazon with variable climate circulation (Meesters, in prep)

21. Would early warning signs be detectable?? Increased variance swamped in overall variance increase Time scale of fluctuation short relative to time scale of change itself No fluctuation signal at all

22. Critical indicators The basis of such a system is long-term monitoring of critical indicators These indicators should be quantities that are relatively accessible, and easy to monitor at high temporal and/or spatial resolution. should represent the variability of the Amazon ecosystem services and other important tipping phenomena => their behaviour near critical transitions should reliably point to imminent change in the state of that particular ecosystem service.

23. Monitoring system WHICH properties of the Amazon would be important to monitor as indicators? – Existing monitoring – Invest in key new ones HOW would we detect imminent change from these? – Advanced statistical techniques looking at variability and ‘slowing down’ – Analyse trends as well and compare with models to extrapolate – Analyse model output as guidance

24. EWS framework In situ monitoring stations Modeling System Remote sensing monitoring system Monitoring, analysis and prediction Communicating alerts Policy responses EARLY WARNING SYSTEM Monitoring system Modeling System Analysis Tools Communications

25. Which institution can do this? Aim: – Manage critical monitoring systems – Re-analyse and re-run coupled forecasts – issue regular ‘state of the Amazon’ reports CEMADEN – Brazil centre for monitoring and forecasting of natural disasters – Federal institute – Runs several intensive monitoring systems – Now short-term forecasting – Could add long-range forecasting branch An independent NGO? – More difficult to obtain information and run monitoring – Could more easily advise against government policy

26. Products for this week? Outline / draft of ‘proposal for an EWS for Amazonia’ Outline for high-level paper on EWS: the science behind it An explicit list of monitoring elements An explicit list of model tools needed

27. Early warning system – draft outline Components: – Critical ecosystem services (WP1) – Requirements from policy/stakeholders (WP4) – Review risk of collapse (WP3) – Framework for threshold typology (theory) – Review of likely thresholds/ behaviour at threshold crossing (so far, from literature) – Indicators (requirements and listing – WP2,3) – Monitoring options/recommendations (WP5) – Analysis tools and forecast models (WP5, WP3) – Institutional embedding (WP5) – Communication means (WP5, WP4, WP6)

28. Implementation EWS Critical Services Policy need Likelihood collapse Types of thresholds Review thresholds Indicators Monitoring Analysis/forecast Institutional Communication Measures (adapt, mitigate)

29. Format Policy report Submittable proposal for tender Paper on concept of EWS Review of most important thresholds depending on time and spatial scale – Joint with WP3/WP2, explore the model results (BESM, IPSL, CMIP3, DGVMs?) – update Nobre&Borma (3 degrees-40%), Good et al (reg thresholds)... – Review socio-economic thresholds / indicators (need WP4 to think about this!) Review on (new) observational and analysis methods for EW? – Only if we can do a TEST. So far not enough data/no tipping points? – Lack of power to detect tipping points (Antoon Meesters)..?

31. List of possible variables to monitor Sea Surface Temperature (SST) - indicator of global-scale change Precipitation (patterns, quantity, dry season length…) primary driver as well as an ecosystem service that can be affected Climate modes (ENSO, Atlantic Oscillations, etc) - often correlated indicators of high-impact changes or episodes in Amazonia River flow and discharge Evapotranspiration - prime driver of recycling

32. List of possible variables to monitor overall vegetation productivity changes – [CO2] over the tropical belt + anthropogenic emissions Monitor a number of permanent plots. Among others, look at PHENOLOGY. Biomass - remote sensing (eg S-band Radar) and well-referenced growth bands in forest plots across the basin Water use efficiency from tree-ring & gas exchange monitoring Remote sensing indices (NDVI, EVI)

33. List of possible variables to monitor Fires (remote sensing and in-situ observations) – not simply occurrence or area, but also fire effects (e.g. type of vegetation affected and recovery of previously burned areas Economic indicators, such as the GDP of the region, transport, trade and migration patterns Exposure and Vulnerability (?)