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Easterling

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Agricultural Adaptation to Climate Change—Is Uncertain Information Usable Knowledge? William E. EasterlingTemperate vs. Tropics: Percent Change in Maize Yield vs. Temperature Change (with/without adaptation) from Modelling Studies (Easterling et al, in preparation for PNAS) Green = with adaptation Red = without adaptation Adaptive capacity exceeded at about +3 deg. CClim ate Change CO 2 fertilization Crop yields Irrigation demand Sens itivity thresholds Commodity prices Hunge r risk Adaptation efficiency/Vulnerability More certa in Less certain Cascading Uncertainty The Double Bind of Adaptation Adaptation will require a steady stream of relia ble scientific information. Scientific information on adaptation is judged unreli able.But why is scientific information on adaptation judged unreliable? • There are too many uncertainties. • “Point estimate” methodologies • But is uncertain information not useful? – Of course it’s useful! • Projected hurricane paths • Surgical outcomes • Even the angle of reentry for the space shuttleBut what makes less reliable information usable knowledge? • Tractable to users – Works in the real world • Is c redible – Passes the sniff test • Is legiti mate – Product of formal scientific protocol • Is s alient – Arrives in time to be useful According to Peter Haas, scientific information is usable knowledge if it meets the following c riteria:Examples of Usable Climate Knowledge • Palmer Drought S everity Index • Heating and Cooling Degree Days • Crop Mois ture Index • Plant hardiness zonesBut most information on adaptation roughly meets those criteria for usable knowledge—so what’s missing? Explicit characterization of uncertai nty! i.e., usable knowledge requires the previous criteria, plus treatment of uncertainty3 Types of Uncertainty (adapted from Mann et al) 1. Fundamental – Situations so novel that no model exists • Nonlinea r systems – E.g., new weed, insect, or pathogen genomesThe First Agricultural Surprise of the 21 st Century? Soybean rust and Hurricane Ivan3 Types of Uncertainty (adapted from Mann et al) 1. Fundamental – Situations so novel that no model exists • Nonlinea r systems – New weed, insect, or pathogen genomes – Energy insecurity0 50 100 150 200 250 1980 1985 1990 1995 2000 2005 2 010 2015 2020 2025 Fuel Proportions to Meet World Energy Fuel Proportions to Meet World Energy Demand, 1980 Demand, 1980 2025 (quadrillion Btu) 2025 (quadrillion Btu) Oil Natural Gas Coal 34% 27% 9% Renewables Nuclear 26% 5% Share of World Total History Projections 38% 24% 24% 8% 6% Source: G. Caruso DOE EIA (February 17, 2007) International Energy Outlook 2006 Energ y Insecurity– Dependence on petroleum from politically fractious regionsDealing with fundamental uncertainty: Enrich SRES with broader environmental and societal possibilities A1 Rapid economic growth with population peaking at 2050 and falling thereafter. Regional convergence of incomes and among cultures. = moderately to very high GHG emissions B1 Same as A1 but stronger transition to service/information economy and to lower material intensity and clean, resource efficient technologies. Sustainability emphasized. = very low GHG emissions A2 Continuous population growth, regional identities emphasized, economic development remains fragmented and slower than A1 and B1. = moderately high GHG emissions B2 Continuous population growth, but lower than A2. Emphasis on local solutions to technological efficiency and sustainability. Slower economic development and technological change. = moderately low GHG emissions2.8° C Global Temperature Projections from Models Structural climate mode l uncertainty Socioeconomic uncertainty (Source: IPCC, 2001)3 Types of Uncertainty (adapted from Mann et al) 2. Structural – Major processes missing or misspecified – Ambiguous or poorly st ated system boundaries (e. g., when does the farmlevel stop and the market level begin?) – Incomplete or competing conceptual modeling frameworks • E.g., when crop models go bad…Optimum temperature curve for plant growth in EPIC and CERES maize 0 20 40 60 80 100 20 22 24 26 28 30 32 34 36 38 40 Temperature 0 C F in al b io m ass (g /p lan t) EPICMaize CERESMaize ~25.5° ~26.5°CO 2 fertilization—when new research increases rather than decreases uncertainty………….CO 2 fertilization: chambers versus FACE rings Why are we so worried t hat CO 2 effects may have been overestimated?0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 2.2 2.4 0 100 200 300 400 500 600 700 800 900 1000 1100 CO2 Concentration (ppm) ALL DATA MEAN of FACE data 100ppm MEAN Long et al. (2006) Leastsquarefit to all data Leastsquare fit to summary data MEAN of OTC data Straightline fit Open symbols are observed data from chambers Shaded circles are from Long et al FACE summary Sensitivity of wheat to [CO 2 ] Chamber results versus Long (source: Tubiello et al., 2006) We don’t see a differe nce!Dealing with Structural Uncertainty • Model intercomparisons • Clear statem ents of boundary conditions of modeling3 Types of Uncertainty (adapted from Mann et al) 3. Value or Paramet ric – Missing, inaccurate or nonrepresentative data – Poorly known or unst able model parameters. – Inappropriate spatial or temporal resolution • Making a climate model talk to a crop model….0 10 20 30 40 5 Kilometers 0 100 200 300 400 500 50 Ki lometers CSIRO GCM Grid Box @ 5° Regional CM Sub-Grid Boxes @ 0.5° 10 km 2 Scale of plant growth model = 1/100 km 2 Scales of Climate Models vs Plant Growth Models •Hi Resolution RegCM % Diff. from Baseline <40 40  30 30  20 20  10 10  0 00  10 10  20 20  30 30  40 >40 Response of Corn Yields (% of 196095 baseline yields) to CSIRO 2xCO 2 Climate Change versus RegCM 2xCO 2 Climate Change With Adaptation Low Resolu tion CSIROA sidebar: farmers as coproducers of usable knowledgeInvolving farmers in the development of climate predictions—the Argentine Pampas Forecast performance · 6 of every 10 years · 7 of every 10 years · 8 of every 10 years Lead time · 6 months · 3 months · 1 month Spatial resolution · Low · Intermediate · High Mode of distribution · Internet · Personal communication · Mass communication Conjoint Analysis Forecast


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