Transcript Slide 1
Institute for Climate and Atmospheric Science Andy Challinor [email protected] Forecasting food in China: the influence of climate, composition and socio-economics Co-authors: Evan Fraser, Steve Arnold, Sanai Li, Elisabeth Simelton China South Asia Lobell et al. (2008). Prioritizing Climate Change Adaptation Needs for Food Security in 2030. Science 319. Turner, B. et al. (2003) A framework for vulnerability analysis in sustainability science. PNAS. 100,4, 8074-8079. Progress in modelling food crop production Simulating a range of impacts based on socioeconomic scenarios Adaptation, e.g. through choice of crop genotype Quantifying biophysical uncertainty: climate and crop yield ensembles Linking crop yield and climate prediction models to assess impact Agrometeorology Qualitative approach to food systems research Attempts to generalize field studies and link with global drivers. DFID’s “sustainable livelihoods approach” that looks at how different types of capital are used to obtain food. Focus on ways people obtain food. ? Qualitative approach to food systems research Biophysical Modelling Climate impacts and adaptation in China Can wheat yield be simulated using a crop model driven by regional climate model (PRECIS) output? What are the drivers of current and future yields? Is adaptation needed? Wheat cultivation in China Winter wheat is partially irrigated in some regions of China (no quantitative data available) Tests at two locations showed better modelobservation agreement for rainfed simulations than irrigated Comparison of simulated and observed wheat yield (kg/ha) at 0.5o scale across China (a) Observations (b) Simulations (rainfed), using PRECIS baseline climate Current climate: simulated wheat yield as a function of seasonal total rainfall in China Climate change: temperature limitations on yield of winter wheat Baseline Grain-filling occurs after flowering Increase in simulated wheat yield (%) in response to a doubling of CO2 from 350 to 700 ppm in China (No associated climate change) Two plausible responses to a doubling of CO2 The ‘net’ effect of climate change in the North China Plain: Results qualitatively similar for A2 and B2 scenarios Mean yield from: Without CO2 North NCP South NCP With CO2 ~20 to 50+ % increase ~20% decrease ~20% increase Interannual variability of yield: CV up by ~10-20% across NCP. Winter wheat Causes of north/south difference: • Increase in the amount of seasonal precipitation in the north – associated decrease in soil water stress • Lengthening of period between flowering and harvest in the north, decrease in much of the south – Super-optimal temperatures – Earlier flowering whilst temperatures are increasing => cooler (sub-optimal) post-flowering temperatures Baseline Genotypic adaptation to climate change Which genotypic properties are needed to adapt to climate change? Do these properties exist in the current germplasm? Ensemble methods: genotypic adaptation to changes in mean temperature, using QUMP Simulation count • Graph suggests 20% increase in TTR is needed • Further simulations and analysis of crop cardinal temperatures suggest a 30% increase may be needed • Simple analysis of field experiments suggests the potential for a 14 to 40% increase within current germplasm Challinor et al., 2008b Response to climate change, from over 180,000 crop simulations for one location 0% Increase in thermal time requirement 10% 20% Percentage change in yield Looking across India: what is the adaptive capacity contained within current germplasm? Yield reduction > 50% 20-30% < 10% 100 75 Area affected (%) 50 Area affected 25 ? Mean T Upper estimate 0 Noadapt • Potential for a 14 to 40% increase within current germplasm Adapt 0% - ?% Challinor (2008): GECAFS proceedings Looking across India: what is the adaptive capacity contained within current germplasm? Yield reduction > 50% 20-30% < 10% 100 75 Area affected (%) 50 All Humidity ? Mean T T extremes Water 25 0 Noadapt Area affected Upper estimate Adapt Challinor (2008): GECAFS proceedings What ‘new’ processes will limit yield in the future? Crops and atmospheric composition: O3 • Industrial emissions resulting in increased surface ozone are predicted to rise. • Predictions for China particularly high. • Ozone lowers the photosynthetic rate and accelerates leaf senescence ~5% yield reductions currently; 30% in 2050? • Few crop field studies with O3 carried out in the tropics See e.g. Long et al. (2005); Slingo et al. (2005) Future air quality and climate closely linked Probability of max 8-h O3 > 84 ppbv vs. daily max. T (USA) Lin et al. (Atm. Env., 2001) Correlation of high ozone with increasing temperature is driven by: (1) Stagnation in the boundary layer, (2) biogenic hydrocarbon emissions, (3) chemical reaction rates, (4) deposition How will these processes interact to determine future air quality in China? Atmospheric composition modelling at Leeds • TOMCAT - State-of-the-art 3D global chemistry-transport model - Offline, so ideal for process studies, comparison with observations, parameterisation development. TOMCAT surface ozone (23 June 2008) • UKCA - Collaboration between universities and Met Office - Coupled climate-chemistry-aerosols - Ozone photochemistry coupled to climate and land-surface - Coupled ozone deposition fluxes and climatic drivers for future Composition-climate-crop strategy TOMCAT ozone fields Stomatal deposition parameterisation for vegetation/crop type Climate drivers (analyses) Offline studies (no climate-chemistry coupling) for evaluation of parameterisations GLAM with O3 flux parameterisation Yield Composition-climate-crop strategy Coupled (climate-chemistry) studies for prediction Stomatal ozone flux UKCA Surface ozone Land surface scheme GLAM with O3 flux parameterisation Yield Climate drivers From Oct 2008: PhD student joint with Met Office – will work on ozonevegetation interactions using TOMCAT and UKCA Composition-climate-crop strategy Stomatal ozone flux UKCA Coupled (climate-chemistrycrop) studies: importance of land use and patterns of deposition Surface ozone Land surface scheme GLAM with O3 flux parameterisation Yield Climate drivers From Oct 2008: PhD student joint with Met Office – will work on ozonevegetation interactions using TOMCAT and UKCA Qualitative approach to food systems research Attempts to generalize field studies and link with global drivers. DFID’s “sustainable livelihoods approach” that looks at how different types of capital are used to obtain food. Focus on ways people obtain food. Analyses of socio-economic drivers of crop productivity • Will farmers have access to the genotypes needed for adaptation? • What characteristics make a food production system vulnerable or resilient to environmental change? Impact of environmental change Health impacts Small problem big impact - did not adapt Economic Impacts Big problem small impact - managed to adapt Harvest Impacts Exposure (e.g. to droughts of different severity) Increasing impact Invest in other agr activities Vulnerable Agr production capital, Invest in agr, GDP share of agr Double cropping Fertiliser, Machinery Rural population Infrastructure RESILIENT Wheat Electricity Increasing exposure Conclusions China South Asia Lobell et al. (2008). Prioritizing Climate Change Adaptation Needs for Food Security in 2030. Science 319. Starting to happen: • NERC QUEST • ESRC Centre for Climate Change Economics and Policy Sustainability Science approach to food systems Qualitative approach to food systems research Biophysical Modelling Summary of observed and modeled increase in wheat yield in response to elevated CO2 Increase in Increasee CO2 ppm in yield (%) Methods Source 330-660 37 Glasshouse or growth chambers Kimball, 1983 350-700 31 Estimated by cubic Amthor, 2000 equation from multiple experiments 350-700 28 Linear extrapolation of FACE experiment Easterling et al., 2005 370-550 7 -23 FACE experiment Kimball,2002 330-660 25 CERES for C3 crops Boote,1994 350-700 16-30 GLAM model Vulnerability trend 1960s-2001 No increase in double cropping (only land increase) Low rural labour - inefficient land use. Land conversion projects: from wheat to rice Heilongjiang Jilin Inner Mongolia Beijing Tianjin Shandong Hebei Ningxia Qinghai Gansu Liaoning Shanxi Shaanxi Henan Sichuan Jiangsu Anhui Shanghai Hubei Zhejian Hunan Fujian Guizhou Yunnan Jiangxi Guangxi Wheat VI-trend (RI>1) wheat 0,025 0,0125 Lowest per capita 0,0025 investments in Guangdong agriculture (highest double cropping). Guangxi highest mean VI (wheat) of all. “Food prices are rising on a mix of strong demand from developing countries; a rising global population; more frequent floods and droughts caused by climate change; and the biofuel industry’s appetite for grains, analysts say.” Also: rising input prices (oil, fertiliser) and speculation (e.g. based on expected demand for biofuel)