Vegetatie-klimaat-terugkoppelingen en de (on)waarschijnlijkheid van kantelpunten

Han Dolman, Antoon Meesters en Ko van Huissteden

Afdeling Aardwetenschappen

Vrije Universiteit Amsterdam

De neerslag-vegetatie terugkoppeling in de Amazone

Wat zeggen de modellen?NATURE GEOSCIENCE DOI: 10.1038/NGEO1741 LETTERS

90° W

23.44° N

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Figure 1 |Map of tropical forest. Shown are tropical land regions and model gridboxes predicted to have more than 85% cover of forest for pre-industrialclimate (continuous and dashed black outlines). The green dots are from satellite retrievals of where there is mainly evergreen tropical forest, based on theGLC2000 land cover map. The gridboxes used in our analysis have continuous black outlines. The 15 gridboxes outlined with dashed lines were notincluded in our analysis as these areas contain little (<10%) observed forest cover, despite the model predicting higher coverage.

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iap_fgoals1_0_gncar_pcm1bccr_bcm2_0ncar_ccsm3_0gfdl_cm2_1csiro_mk_3_0inmcm3_0gfdl_cm2_0cnrm_cm3ingv_echam4mri_cgcm2_3_2aukmo_hadgem1giss_e_hgiss_e_rcccma_cgcm3_1miroc3_2_medresipsl_cm4miub_echo_gcsiro_mk_3_5miroc3_2_hiresukmo_hadcm3mpi_echam5

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bccr_bcm2_0ncar_ccsm3_0inmcm3_0cnrm_cm3giss_e_hcsiro_mk_3_0mri_cgcm2_3_2agiss_e_ripsl_cm4ingv_echam4gfdl_cm2_0gfdl_cm2_1miub_echo_gcsiro_mk_3_5cccma_cgcm3_1ukmo_hadgem1miroc3_2_hiresmiroc3_2_medresmpi_echam5ukmo_hadcm3

iap_fgoals1_0_gncar_ccsm3_0ncar_pcm1bccr_bcm2_0ukmo_hadgem1miroc3_2_medresinmcm3_0csiro_mk_3_0gfdl_cm2_1cnrm_cm3giss_e_hmri_cgcm2_3_2agfdl_cm2_0giss_e_ringv_echam4csiro_mk_3_5miub_echo_gipsl_cm4cccma_cgcm3_1miroc3_2_hiresmpi_echam5ukmo_hadcm3

a b c

Figure 2 | Biomass change. a–c, Tropical forest biomass predictions for the Americas (a), Africa (b) and Asia (c) by the MOSES–TRIFFID model forced by22 climate models. Climate models emulated are colour-coded, from dark blue to dark red for decreasing year 2100 values of Cv. Grey regions and squaresare committed Cv values with climate constant at year 2100 values, and small dashes link back to the same model in transient predictions. Committedequilibrium values are year-independent, hence the x-axis break (small vertical bars). Normalized estimates of Cv from inventory data (2.5%, mean and97.5% confidence levels) are the short black curves for Americas and Africa. Horizontal lines (large dashes) are estimated pre-industrial values, year 1860.

This is for the three tropical regions, to year 2100 and drivenwith atmospheric [CO2] concentrations and non-CO2 radiativeforcing pathways representative of the Special Report on EmissionsScenarios (SRES) A2 business-as-usual anthropogenic emissionsscenario. These predictions have been constructed by emulatingthe changes in surface meteorology predicted by the 22 climatemodels, all in the combined climate and land surface impactssystem IMOGEN (ref. 14; Methods). Such changes of climateare added to the CRU climatology, taken as representative ofpre-industrial conditions and removing significant model biases(Supplementary Fig. S1). For the contemporary period,Cv increasesin all simulations and regions, and is compared with normalizedforest inventory data (Methods) as the three short black curves forAmericas and Africa. The three curves correspond to changes at the97.5% confidence level, mean change and 2.5% level12,13. There isagreement that tropical forests are gaining biomass, although theobservational data suggest the increases have been larger than thatmodelled for the recent period. The magnitude of the increase intropical forest biomass from plot networks is the subject of somedebate15. However, the contemporary increase in tropical forestbiomass is consistent with the large and increasing carbon sink onEarth’s land surface derived from the mass-balance implicationsof fossil-fuel CO2 emissions and atmospheric CO2 measurements,along with the global role of woody tissue as the location of a largefraction of the terrestrial carbon sink6.

Forest biomass carbon stocks in Asia and Africa are projected tobe greater in year 2100 than at the present day, in all simulations.This is also true for the Americas/Amazon, except for the HadCM3climate model. There is however a decreasing ability to sequestercarbon in biomass; many pathways have a Cv peak towards the endof the twenty-first century. Figure 2 grey columns are commitmentsimulations where climate forcing (here, predicted for 2100) ismaintained at that level for a sufficient period that terrestrialecosystems fall in equilibrium with that amount of climate change.Generally this increases the spread of simulations, where thosewith higher vegetation carbon at the end of the twenty-firstcentury show an even higher uptake for the committed period, andsimulations peaking earlier in the century show a further reduction.Particularly large differences between the final year of the transientsimulations and committed values of Cv are, for Americas: majorbiomass loss for HadCM3 (confirming the analysis of ref. 16); andthe MPI ECHAM 5 model predicts less Cv than that estimatedin pre-industrial times.

We perform sensitivity simulations where only single patternsof meteorological change are added to the CRU climatology.This aids understanding of the mechanisms responsible for thechanges in Cv. Figure 3 shows these changes, years 1860–2100,for the Americas/Amazon region, and decomposes them intothe individual effects of temperature, rainfall and atmospheric[CO2]. Predictions are most sensitive to changes in temperature

NATURE GEOSCIENCE | VOL 6 | APRIL 2013 | www.nature.com/naturegeoscience 269

NATURE GEOSCIENCE DOI: 10.1038/NGEO1741 LETTERS

90° W

23.44° N

0.00

23.44° S

60° W 30° W 0° 30° E 60° E 90° E 120° E 150° E

Figure 1 |Map of tropical forest. Shown are tropical land regions and model gridboxes predicted to have more than 85% cover of forest for pre-industrialclimate (continuous and dashed black outlines). The green dots are from satellite retrievals of where there is mainly evergreen tropical forest, based on theGLC2000 land cover map. The gridboxes used in our analysis have continuous black outlines. The 15 gridboxes outlined with dashed lines were notincluded in our analysis as these areas contain little (<10%) observed forest cover, despite the model predicting higher coverage.

2000 2050 2100

iap_fgoals1_0_gncar_pcm1bccr_bcm2_0ncar_ccsm3_0gfdl_cm2_1csiro_mk_3_0inmcm3_0gfdl_cm2_0cnrm_cm3ingv_echam4mri_cgcm2_3_2aukmo_hadgem1giss_e_hgiss_e_rcccma_cgcm3_1miroc3_2_medresipsl_cm4miub_echo_gcsiro_mk_3_5miroc3_2_hiresukmo_hadcm3mpi_echam5

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Americas vegetation carbon Africa vegetation carbon Asia vegetation carbon

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iap_fgoals1_0_gncar_ccsm3_0ncar_pcm1bccr_bcm2_0ukmo_hadgem1miroc3_2_medresinmcm3_0csiro_mk_3_0gfdl_cm2_1cnrm_cm3giss_e_hmri_cgcm2_3_2agfdl_cm2_0giss_e_ringv_echam4csiro_mk_3_5miub_echo_gipsl_cm4cccma_cgcm3_1miroc3_2_hiresmpi_echam5ukmo_hadcm3

a b c

Figure 2 | Biomass change. a–c, Tropical forest biomass predictions for the Americas (a), Africa (b) and Asia (c) by the MOSES–TRIFFID model forced by22 climate models. Climate models emulated are colour-coded, from dark blue to dark red for decreasing year 2100 values of Cv. Grey regions and squaresare committed Cv values with climate constant at year 2100 values, and small dashes link back to the same model in transient predictions. Committedequilibrium values are year-independent, hence the x-axis break (small vertical bars). Normalized estimates of Cv from inventory data (2.5%, mean and97.5% confidence levels) are the short black curves for Americas and Africa. Horizontal lines (large dashes) are estimated pre-industrial values, year 1860.

This is for the three tropical regions, to year 2100 and drivenwith atmospheric [CO2] concentrations and non-CO2 radiativeforcing pathways representative of the Special Report on EmissionsScenarios (SRES) A2 business-as-usual anthropogenic emissionsscenario. These predictions have been constructed by emulatingthe changes in surface meteorology predicted by the 22 climatemodels, all in the combined climate and land surface impactssystem IMOGEN (ref. 14; Methods). Such changes of climateare added to the CRU climatology, taken as representative ofpre-industrial conditions and removing significant model biases(Supplementary Fig. S1). For the contemporary period,Cv increasesin all simulations and regions, and is compared with normalizedforest inventory data (Methods) as the three short black curves forAmericas and Africa. The three curves correspond to changes at the97.5% confidence level, mean change and 2.5% level12,13. There isagreement that tropical forests are gaining biomass, although theobservational data suggest the increases have been larger than thatmodelled for the recent period. The magnitude of the increase intropical forest biomass from plot networks is the subject of somedebate15. However, the contemporary increase in tropical forestbiomass is consistent with the large and increasing carbon sink onEarth’s land surface derived from the mass-balance implicationsof fossil-fuel CO2 emissions and atmospheric CO2 measurements,along with the global role of woody tissue as the location of a largefraction of the terrestrial carbon sink6.

Forest biomass carbon stocks in Asia and Africa are projected tobe greater in year 2100 than at the present day, in all simulations.This is also true for the Americas/Amazon, except for the HadCM3climate model. There is however a decreasing ability to sequestercarbon in biomass; many pathways have a Cv peak towards the endof the twenty-first century. Figure 2 grey columns are commitmentsimulations where climate forcing (here, predicted for 2100) ismaintained at that level for a sufficient period that terrestrialecosystems fall in equilibrium with that amount of climate change.Generally this increases the spread of simulations, where thosewith higher vegetation carbon at the end of the twenty-firstcentury show an even higher uptake for the committed period, andsimulations peaking earlier in the century show a further reduction.Particularly large differences between the final year of the transientsimulations and committed values of Cv are, for Americas: majorbiomass loss for HadCM3 (confirming the analysis of ref. 16); andthe MPI ECHAM 5 model predicts less Cv than that estimatedin pre-industrial times.

We perform sensitivity simulations where only single patternsof meteorological change are added to the CRU climatology.This aids understanding of the mechanisms responsible for thechanges in Cv. Figure 3 shows these changes, years 1860–2100,for the Americas/Amazon region, and decomposes them intothe individual effects of temperature, rainfall and atmospheric[CO2]. Predictions are most sensitive to changes in temperature

NATURE GEOSCIENCE | VOL 6 | APRIL 2013 | www.nature.com/naturegeoscience 269

Huntingford et al, 2013

AMAZALERT D3.1 CMIP5 projections

12

In Amazonia, some climate and vegetation models project a shift in biome from tropical forest to seasonal forest or savanna by the end of the 21st century driven by regional climate change – increases in temperature and decreases in rainfall – in the absence of direct deforestation (Cox et al. 2000; Betts et al. 2004; Scholze et al. 2006; Malhi et al. 2009; Salazar et al. 2007; Salazar and Nobre 2010). Malhi et al. (2009) found that the CMIP3 climate changes in the region tend to push the region towards a seasonal forest-type regime rather than savanna. Their results suggest a high probability of an intensification of the seasonality of the rainfall in Amazonia, leading to increased dry season water stress, and a medium probability that this new state will favour seasonal forest over tropical forest. They suggest that some of the negative effects of the climate change on the forest may be mitigated by ecosystem response to rising CO2 and changing climate, although there are large uncertainties associated with this. Rammig et al. (2010) found from 24 general circulation models projections that the uncertainty associated with the long-term effect of CO2 is much larger than that associated with precipitation change and concluded that CO2 effects are one of the key unknowns in assessing the risk of Amazonian forest dieback in response to 21st-century climate change. Some more recent studies (Cox et al. 2013; Huntingford et al. 2013 accepted) have suggested that the Amazon forest could be more resilient to CO2-driven climate change than some of the earlier work suggested. However, many of these studies emphasise that climate change is only one potential source of stress on the forest, and land use change in the Amazon may interact with climate changes to produce larger effects namely on the regional climate and hydrology. There is some observational evidence to that effect (Costa et al. 2003; Butt et al. 2011) and many modelling studies have demonstrated these potential effects through a range of experiments (Nobre et al., 1991; Costa and Foley 2000; Cox et al. 2000; Costa et al. 2007; Sampaio et al. 2007; Malhado et al. 2010). Overall, a common argument is that large-scale deforestation alters surface albedo, and evapotranspiration (associated with a decrease in leaf area index, a decrease in root depth, and reduction of roughness), ultimately reducing precipitation totals in the Amazon (Nobre et al., 1991; Costa and Foley 2000; Costa et al. 2007; Sampaio et al. 2007), and may increase the duration of the dry season (Costa and Pires 2010). This could translate in changes in fire frequency (Nepstad et al., 1999) and nutrient cycle feedbacks (Senna et al. 2009), which could lead to changes in vegetation composition and structure (Cox et al. 2000; Malhi et al. 2009; Rammig et al. 2010; Salazar and Nobre 2010), affect water resources (Coe et al. 2009) and carbon emissions (Cox et al. 2004).

Figure 3. Percentage change in forest cover by late 21st century compared with pre-industrial conditions, as modelled using Hadley Centre coupled climate-carbon model HadCM3LC with a ‘business as usual’ greenhouse gas concentration scenario. Red colours indicate a reduction in forest cover. It demonstrates the ‘dieback’ of the forest resulting from simulated warmer and drier climate in the future, and carbon cycle feedbacks. After Cox et al. (2000).

Cox et al., 2002

“Dieback” afhankelijk van klimaat scenario

AMAZALERT D3.4 Risk of collapse

5

climate change, with potentially greater losses to be realised beyond the transient response. It implies a degree of 'temporary resilience'. This could provide an opportunity for rapid mitigation action to reduce the likelihood of dieback. This depends on the timescales of forest response to climate change. It may be that model simulated time scales are biased due to missing mortality processes such as drought or fire. Temporary resilience may thus depend on the return period of extreme drought/fire seasons.

Modelling drivers of critical change Global change: Different ‘pathways’ allow alternative scenarios of emissions to be explored, most recently through CMIP5 (Coupled Model Intercomparison Project phase 5). However, owing to great uncertainty in the terrestrial carbon cycle feedback, there is likewise great uncertainty in the transformation of emissions into atmospheric concentrations, something that has not received much attention to date. Carbon dioxide (CO2): CO2 fertilization confers significant benefits to Amazon forest carbon uptake and increased carbon storage in the models, but this process is a key uncertainty marked by a lack of understanding of how this operates in tropical vegetation and in conjunction with other nutrient and radiation availability. The planned Amazon FACE (Free-Air CO2 Enrichment) experiments could increase understanding and reduce uncertainty in this area. Temperature: Temperature increase is a common feature of climate projections, and considered alone has a negative effect on forest health. However, poorly-represented temperature dependency of respiration and photosynthesis is likely to make most if not all models too sensitive to high temperatures. Ongoing observational work, including through AMAZALERT, should help to develop better model representation of this process. Drought and dry season characteristics: Droughts such as 2005 and 2010 as well as imposed drought experiments have demonstrated that the forest is sensitive to these conditions. The

Figure ES1. Changes in number of grid boxes containing Amazon forest (broadleaf tree fraction > 0.4 within the region 40°W-70°W, 15°S-5°N) in a ‘perturbed parameter’ ensemble of the coupled climate-carbon cycle model HadCM3C. (a) Time series of transient changes for each individual member of the ensemble. (b) Box and whisker plots for each scenario showing the median, inter-quartile range and minimum and maximum values (excluding outliers, black circles).

Kay et al., 2014

Hoe goed representeren die modellen de terugkoppelingen?

% land surface showing “feedbacks” % land surface showing “feedbacks”

Overschatting van de terugkoppeling t.o.v. waarnemingenVilasa-Abad et al., submitted

Neerslag

Vocht aanvoerVerdamping

H

Straling

Soil moisture

GrenslaaghoogteLanggolvige

straling

Naar d’Andrea et al. 2006, GRL. Gemodificeerd voor Tropisch regenwoud

VoorgeschrevenPrognostisch

Analyse met wat simpelers…

1.4

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Wat is het kantelpunt voor de Amazone?

Neerslag

Temperatuur

Tropisch, natTropisch, droog

Huidige verdamping

Kantelpunt

Wat gebeurt er bij een langzame verandering van neerslag?

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G B,W( ) = αB Bopt W( ) – B( ) 

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Meesters and Dolman, in prep

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Met en zonder kantelpunt

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De afhankelijk van B op W en de brand term maakt dat niet alleen de biomassa schuift, maar ook W. Het effect is dat de bodem van het dal ook beweegt en additionele fluctuaties levert.

“Early warning”

• In het geval van de Amazone is de tijdschaal van weerbaarheid korter dan die van de verandering (klimaat, ontbossing): geen early warning

• Interactie van bodemvocht met biomassa geeft andere EW signalen

• In het “no tipping point” geval wel (valse) signalen

Meesters and Dolman, in prep

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Permafrost wel een kantelpunt?

Koolstof voorraden in Arctische gebieden

Schuur et al. Nature 520, 1-9(2015) doi:10.1038/nature14338

CARVE Experiment NASA

Permafrost processen en terugkoppelingen

J van Huissteden, AJ Dolman, Current Opinion in Environmental Sustainability, Volume 4, Issue 5, 2012, 545–551, http://dx.doi.org/10.1016/j.cosust.2012.09.008

Schuur et al. Nature 520, 1-9(2015) doi:10.1038/nature14338

Gemodelleerd koolstof verlies in 2100, 2200, and 2300.

Dooi van ijsrijke permafrost

Belangrijke methaan bron

Wordt aangenomen dat ze in aantal toe gaan nemen (is ook vastgesteld)

Meeste dooimeren dateren uit het Holoceen

Dooimeren

Meer vorming Uitbreiding meer Drainage en opvulling Hergroei permafrost en grondijs

Modeleren van meervorming op landschapsschaal

Van Huissteden et al., Nat. Clim. Change, 2011

Verdeling van meren

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both test areas. A: simulated thaw lakes for one model run. B: Ice content

at the end of the model run. The ice content shows overlapping drained thaw

lake basins, characterized by di�erent soil ice content. C: Average thawed

area (blue) and drained area (red) evolution through time, averaged over 10

model runs. The grey lines represent the standard deviation of the thawed

area. D: Climate input data (mean annual air temperature, for complete

climate data see Supplemental Information). E: Size and shape (isoperimetric

index) distribution of the modelled lakes.22

Verdeling klopt goed

Realistische landschaps- ontwikkeling: grote meren ver weg van de rivier

Van Huissteden et al., Nat. Clim. Change, 2011

Wat gebeurt er in de toekomst?

19

Na 80 jaar is ongeveer 25% met meren bedekt (max)

Verdere uitwerking wordt ingeperkt door drainage: dit geeft een limiet aan de CH4 emissies van 3.5 Tg Ch4 /jaar.

Dat is een factor 10 lager dan andere studies die complete dooi aannemen.

Van Huissteden et al., Nat. Clim. Change, 2011

Controlplot2010

Samecontrolplot2014

Removalplot2010

Sameremovalplot2014

Nauta et al, Nat. Clim Change, 2014

Permafrost dynamiek, hydrologie en emissies

NATURE CLIMATE CHANGE DOI: 10.1038/NCLIMATE2446 LETTERS

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Figure 3 | Relative surface elevation, snow depth, groundwater level and methane flux in control and B. nana removal plots in 2012. a–d, Data are meanvalues ± s.e.m., n=5 plots for relative surface elevation (a), snow depth in April (b), groundwater level in mid-July (c) and methane flux in early August (d).The legend in a applies to all panels. (⇤), ⇤ and ⇤⇤ indicate significant di�erences between the two treatments (p<0.10, p<0.05, p<0.01, respectively).Positive methane (CH4) fluxes indicate net emissions of methane to the atmosphere; negative values indicate net uptake of methane from the atmosphere.

ThawingSoil subsidence

Trapping snow, water

Methane

Reduced shrub cover

+

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Shrub-dominated Graminoid-dominated

Figure 4 | How elevated shrub patches turned into graminoid-dominatedwet depressions after removal of B. nana shrubs. a, Schematicrepresentation of the permafrost collapse that took place in the B. nanaremoval plots. b, The positive feedback loop triggered by reduced shrubcover and the implications for the methane balance, all measured in thefield experiment.

composition, microtopography, snow and hydrology, shownin this study, are prominent features of the lowland tundralandscape, which help protect the permafrost but also make thetundra vulnerable to disturbance9,23,24. It was not only within ourexperimental plots that the permafrost collapsed. We observedseveral thaw ponds with drowning B. nana shrubs in the study area.The trigger for the small-scale permafrost collapse in these ‘natural’thaw ponds was often unclear. Perhaps a gap in the protectiveshrub cover due to ageing of shrub plants or increases in plantpathogen pressure25 could initiate the positive feedback loop.Climate warming, flooding and destruction of vegetation have alsobeen suggested as triggers causing permafrost collapse features26–28.

Observations from all over the Arctic suggest increased shrubgrowth coinciding with summer warming during recent decades29.However, if increased permafrost thawing can more frequentlytrigger small-scale permafrost collapse,methane-emittingwet sedge

depressions might become more abundant in the landscape, at thecost of permafrost-stabilizing low shrub vegetation, at least in poorlydrained ice-rich permafrost regions. Increasing wetland extent dueto increased small-scale permafrost collapse in the Arctic tundraregion seems an underestimated trajectory in response to climatewarming4,30. Our findings show that even disturbance of part ofthe vegetation can trigger feedback loops that cause permafrostcollapse and an ecosystem shifting from being a methane sink to asource. They have far reaching implications for all human activitiesin the Arctic, for example, oil and gas field development, which maydestroy the vegetation, and for the Arctic greenhouse gas balance,with potential feedbacks to global climate warming.

MethodsStudy site. The study was conducted at the Chokurdakh Scientific Tundra Station(70� 490 N, 147� 290 E, 10m above sea level) located in the Kytalyk (Siberiancrane) Wildlife Reserve in the Indigirka lowlands in north-eastern Yakutia,Russian Federation. Chokurdakh mean annual air temperature is �13.4 �C(1981–2010), with �34.0 �C mean January temperature and 10.3 �C mean Julytemperature. Mean annual precipitation is 196mm (1981–2010), of which 76mm(39%) falls in the summer months (June to August). The study area is underlainby thick continuous permafrost with a high ice content (>20% by volume)20,which reaches more than 300m depth. The field experiment was set up in adrained lake basin, which consists mostly of elevated shrub patches dominated byBetula nana L. (dwarf birch) shrubs surrounded by a di�use drainage network ofwet depressions dominated by the graminoid species Eriophorum angustifoliumHonck. See Supplementary Methods for a more extensive site description.

Experimental design. We established ten circular plots of 10m diameter in theelevated B. nana patches within the drained thaw lake basin in summer 200710.Plots were selected pairwise, on the basis of proximity and similarity invegetation composition and shrub density. Subsequently, the two treatments wererandomly assigned to the plots within each pair: a control treatment (no shrubremoval) and a removal treatment. Aboveground biomass of B. nana wasremoved by clipping stems at the moss surface to minimize disturbance. Tomaintain the B. nana removal treatment, regrowth of B. nana was removed insummer 2010. It was not necessary to remove the regrowth every year. One yearafter removal there was very limited regrowth, but by the third year regrowth wassubstantial in all removal plots and therefore removed. Regrowth could occur asthe stems were clipped o� at the moss surface, so new shoots could develop fromremaining belowground resources in coarse roots. Only permanently wetconditions seemed to prevent recolonization.

Measurements. At the start of the experiment before manipulation, plant speciesabundance measurements and aboveground biomass harvests have been made forall ten plots. In addition, soil thawing depth, soil moisture and soil temperatureswere measured during experiment establishment in mid-July 2007. Active layerthickness, using a bluntly tipped metal probe, and plant species abundances,

NATURE CLIMATE CHANGE | ADVANCE ONLINE PUBLICATION | www.nature.com/natureclimatechange 3

Nauta et al, Nat. Clim Change, 2014

2 NATURE CLIMATE CHANGE | www.nature.com/natureclimatechange

SUPPLEMENTARY INFORMATION DOI: 10.1038/NCLIMATE2446

Supplementary Figure 1. Pictures of a removal plot in 2007 and 2012. a, shows the B. nana

removal plot at the start of the experiment in 2007 (after clipping). b, shows the appearance of

a pool and invasion of flowering graminoids in 2012. The 10-m diameter plot area is

delineated by the white dashed line.

a b

Regeneratie en negatieve feedbacks

J van Huissteden, AJ Dolman, Current Opinion in Environmental Sustainability, Volume 4, Issue 5, 2012, 545–551, http://dx.doi.org/10.1016/j.cosust.2012.09.008

J van Huissteden, AJ Dolman, Current Opinion in Environmental Sustainability, Volume 4, Issue 5, 2012, 545–551, http://dx.doi.org/10.1016/j.cosust.2012.09.008

Wat betekent dit alles?• Niet altijd zijn early warning signalen of

kantelpunten te vinden (Amazone) en het gedrag van complexe systeem is vaak wat ingewikkelder dan de makkelijke analyses

• Onze “grote” ESM’s lijken niet altijd de juiste terugkoppelingen te hebben

• Terugkoppelingen van vegetatie, hydrologie zijn in staat om negatieve terugkoppelingen te generen die het systeem wellicht meer linear maken