The uncertainties within the framework of impact studies of climate change on agroecosystems

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1 The uncertainties within the framework of impact studies of climate change on agroecosystems Suggestion of a typology Illustrations with some results of the project Nadine Brisson(INRA), Denis Allard (INRA), Michel Déqué(Météo France) 1 Uncertainty in our everyday life Does not make us comfortable Difficulty or inability to forecast Behaviors of indetermination, hesitancy 2

2 Uncertainty in physics Until the XX century Determinism vision of the world (Newton) One cause = one effect Revolution during the XX century Quantum mechanics (µscopic scales) One cause = effects with a probability function Heisenberg s principle of uncertainty The butterfly effect small differences in the initial condition of a dynamical system may produce large variations in the long term behavior of the system (Poincarré) dependence on initial conditions in chaos theory (meteorology) Classical or quantum theoretical framework 3 45 references from the period The framework Origin of uncertainties Proposed typology Bibliographic references 4

3 Diapositive 4 pb2 Pas compris : "No prediction" pbertuzzi; 24/09/2010 The framework 1)The impacts Agricultural or environmental land use (Rounsevellet al., 2003; Sitchet al., 2008; Badeauet al., 2010) yield (Izaurralde et al., 2003; Lobell et al., 2006; Challinor and Wheeler, 2007; Lobell and Field, 2008; Masutomiet al., 2009; Niuet al., 2009; Tao et al., 2009; Guoet al., 2010) soil C sequestration (O Neil and Melkinov,, 2008 ; Post et al., 2008; Sitchet al., 2008; Ogle et al., 2010) hydrology (Ducharne et al.,2007; Krysanova et al., 2007;Faramazi et al., 2008) erosion ( Zhang, 2008; Zhang et al., 2010) GHG emission (Sitchet al., 2008; Hastings et al., 2010) 5

4 The framework 2) Methods Models which chain future climate projections with simulation of the impacts either process-oriented and dynamic : Izaurralde et al., 2003; Ducharne et al.,2007; Sitchet al., 2008; Zhang, 2008; Masutomiet al., 2009; Tao et al., 2009; Guoet al., 2010; Zhang et al., 2010 or statistical : Iglesias et al., 2000; Rounsevell; et al., 2003, 2003; Lobellet al., 2006; Lobelland Burke 2008; Badeauet al., 2010 Neither expert-based studies nor analog studies (in particular spatial analogs) evoked in this presentation 6 Two main causes to uncertainty Origin of uncertainties Lack of certainty on the future : requires exhaustive analyses to be able to account for a thorough ensemble of possible futures Estimation error : needs to be reduced as much as possible Present time Future (experimental troubles : FACE, OTC, ) Two questions How to account for uncertainties? How to reduce uncertainties? 7

5 Lack of certainty on the future 1) In relation with our prospective vision at various scales Global : SRES IPCC or other scenarios of GHG emissions Iglesias et al., 2000; Izaurraldeet al., 2003; Rounsevellet al., 2006; Ducharneet al.,2007; Challinorand Wheeler, 2008; Lobelland Burke 2008; Sitchet al., 2008; Zhang, 2008; Masutomiet al., 2009; Tao et al., 2009; Guoet al., 2010; Zhang et al., 2010 Regional : land and soil uses Krysanovaet al., 2007; Faramaziet al., 2008; O Neil and Melnikov, 2008; Post et al., 2008; Niuet al., 2009;Hasting et al., 2010 Field : cropping or forest systems, genotypes, practices Iglesias et al., 2000; Lobellet al., 2006; Challinorand Wheeler, 2007; Krysanovaet al., 2007; Lobelland Burke, 2008; Post et al., 2008; Guoet al., Matusomiet al., 2009; Niuet al., 2009; Ogle et al., 2010 Regional and field scales are typically the scales of adaptation: it seems relevant to consider them as sources of variability standing for potential adaptation decisions of the stakeholders and farmers. 8 Lack of certainty on the future 2) In relation with the chaotic character of climate inter-annual variability Niuet al., 2009 Effect of initial conditions (butterfly effect) Use of weather forecasts (specific to adaptation studies) Stone and Meinke, 2005; D Orgeval et al.,

6 Estimation errors 1) In relation with our imperfect knowledge of processes (and their interactions) imbedded in models (epistemic uncertainties) Climate modeling (large scale) GCM(climate physics, biosphere physics, ocean-atmosphere coupling, empirical relationships embedded, parameters, spatial resolution) Impact modeling (small scale in general; exception = DGVM) Structure: thorough accounting for climate connected processes in the long term and their interactions Parameterization: shifting of parameters due to new climate conditions (either functional adaptation or extension of the validity domain) 10 Estimation errors 2) In relation with imperfect knowledge of spatial realities Input prescription (land use, soil, crop management : Li et al., 2010) Bridging the gap in inconsistent modeling scales GCM downscaling (process-based or statistical including weather generators : Zhang, 2008) Impact model upscaling(metamodels, transfer functions : Stone and Meinke, 2005; Rounsevell et al., 2006; Challinor and Wheeler, 2007) 11

7 Estimation errors : uncertainties to be reduced 3) But how to reduce them? Use of actual data : experiments that mimic future like FACE, statistics from the theoretical starting point of CC Not possible to reduce them in the absence of future measurements : accounting for them need to be estimated need to be classified 12 Estimation of process-oriented epistemic uncertainties when no real data are available Ensemble modeling : running several models supposed to simulate the same reality Climate (Christensen et al., 2002; van derlinden and Mitchell, 2009) and propagation to impacts (Iglesias et al., 2000; Lobellet al., 2006; Lobelland Burke, 2008; Zhang, 2008; Zhang et al., 2010, Tao et al., 2009; Masutamiet al., 2009) Impact (Ewertet al., 2002; Challinorand Wheeler, 2007; Sitchet al., 2008) Perturbation of parameters (activation/inactivation of modules) of one model Up-till-now, Climate impact (Iglesias et models al., 2000) are not strictly comparable in terms of accounting Impact for (Challinoret climate Wheeler, connected 2007; Challinoret processes. al., 2007; Krysanovaet al., 2007; O Neil and Melnikov, 2008; Post et al., 2008; Sitchet al., 2008;Masutomi et al.,2009; Tao et al., Urgent 2009; requirement Hastings et al., 2010; for benchmarking Ogle et al., 2010) actions at an international level 13

8 Illustrations with results from the project CLIMATOR Multi-cropping, multi-sites French project ( ) Various types of uncertainties Identification of the various addressed uncertainties How it was accounted for uncertainties following a rational questioning (not just noise on results) 14 Diapositive 14 pb1 Pas compris : "No prediction" pbertuzzi; 24/09/2010

9 Regional prospective uncertainty : source of variability towards regional adaptation Multi-sites approach at the field scale The spatial approach relies on 13 sites standing for French climate variability for which 30-year climatic series are available ( ) All systems simulated in all sites (except for the tropical one) Guadeloupe Mons en Chaussées Versailles Rennes Mirecourt Dijon Rennes Clermont-montagne Clermont-plaine Bordeaux Avignon Toulouse Colmar 15 Field prospective uncertainty : source of variability towards field adaptation Various cropping systems Annual crop systems : monocropsand rotations of wheat, sunflower, maize, sorghum, rapeseed Perennial systems : grasslands, forest, vineyard including two tropical systems : banana, sugar cane, At various input level : rainfed and irrigated, conventional and organic framing 16

10 Two future periods with respect to one baseline period Inter-annual variability : chaos Baseline Near future Far future SRES A1B Epistemicby ensemble modeling GLOBAL CLIMATE MODEL DOWN-SCALING chaos ARPEGE (2 initial conditions) Epistemic : Ensemble modeling and parameter perturbation IMPACT MODEL A2 B1 4 IPCC models Global prospective uncertainty Field or regional source of variability towards field adaptation INCOMPLETE PROTOCOL 18

11 Accounting for uncertainties following a rational questioning What are the impacts of the various climatic uncertainty components? Which is the weight of epistemic uncertainties and interannual variability compared to climate change trend? Is there an effect of spatial scale? Which is the weight of the farmers potential adapting practices? Is the impact of climate change homogeneous throughout cropping systems and French locations? 19 Climate uncertainties on wheat yield Baseline init (STICS model in Colmar) Envelops of medians for far future Far future init DOWNSCALING DOWNSCALING SRES GCM SRES GCM 20

12 Comparing uncertainties and climate change trend ALL FRENCH LOCATIONS IMPACT OF VARIOUS UNCERTAINTIES ON SUNFLOWER YIELD VARIABILITY (t ha-1) : ANOVA decomposed by factor other interactions interaction with CC simple effects SOUTHERN FRANCE other interactions interaction with CC simple effects Effect of 1.5 scale 1.0 Climate change signal Inter-annual variability Epistemic errors 3 southern sites or all sites, 2 models (STICS and SUNFLO), 2 genotypes, 3 downscaling methods (GCM ARPEGE, SRES A1B) 2 periods (BL and FF) standing for CC, 3 soils. Weight of the farmers or stakeholders potential adaption IMPACT OF VARIOUS UNCERTAINTIES ON SUNFLOWER and WHEAT YIELD VARIABILITY (t ha-1) : ANOVA decomposed by factor Sunflower other interactions interaction with CC simple effects Wheat (Panoramix) Climate change signal 1.5Adaptation other interactions interaction with CC simple effects southern sites or all sites, 2 models (STICS and SUNFLO), 2 genotypes, 3 downscaling methods (GCM ARPEGE, SRES A1B) 2 periods (BL and FF) standing for CC, 3 soils.

13 Is the impact of climate change homogeneous throughout cropping systems and French locations? Yield projections : Near future - Baseline 23 Main results of the project about the climate change signal In some cases (spatial scales, crops),uncertainties can be greater than climate change signal The inter-annual variability of climate remains important and make some average trend results non significant. No single climate change signal through cropping systems and sites 24

14 Main results of the project about agronomic uncertainties The agronomic uncertainty is of the same order of magnitude as the climatic uncertainty Important interaction with sources of variability (crops, soils and sites) which render blind (simple statistic) treatment of results difficult 25 Thank you for your attention 26