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Climate Change Scenarios for Agriculture

Climate Change Scenarios for Agriculture. Sam Gameda and Budong Qian Eastern Cereal and Oilseed Research Centre Agriculture and Agri-Food Canada Ottawa, Canada. Objective.

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Climate Change Scenarios for Agriculture

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  1. Climate Change Scenarios for Agriculture Sam Gameda and Budong Qian Eastern Cereal and Oilseed Research Centre Agriculture and Agri-Food Canada Ottawa, Canada

  2. Objective • Support the elaboration of climate change scenarios and research on climate variability at the national level (and summarize them at a regional scale to support the formulation of adaptation strategies for agricultural areas) • Review of climate change scenarios being developed at global, regional and national levels, and suitable for agricultural impact and adaptation assessments

  3. Climate Change Scenarios • Range of efforts on developing and using climate change scenarios • Global, Regional • IPCC AR4 • EU, ENSEMBLES (PRUDENCE, STARDEX, MICE) • National • US • Climate Change Science Program, Effects of Climate Change on Agriculture, Land Resources, Water Resources and Biodiversity (2008) • Climate Change Impacts for the Conterminous US (Climatic Change 2005 (Vol 69)) • UK, Climate Impacts Program • UKCIP02, UKCIP08 • Developing Countries • UNDP Climate Change Country Profiles (52 countries) • Canada – Climate Change Scenarios Network • Others (China, Australia, New Zealand)

  4. Highlights • ENSEMBLES – Research and Application Function • Probabilistic estimate of uncertainty in future climate • Seasonal, decadal, + • Tool for statistical downscaling • Regional climate data sets Work linked to Evaluation of, and recommendation on, systematic errors in GCM and RCM modelling - higher resolution dynamical and/or statistical downscaling to provide projections and hindcasts

  5. Highlights • PRUDENCE • High resolution climate change scenarios for 2071-2100 for Europe using regional climate models • Estimates of variability and level of confidence in the scenarios • STARDEX • Intercomparisons of statistical, dynamical and statistical-dynamical downscaling methods • Reconstruction of observed extremes • Construction of scenarios of extremes • MICE • Direct use of climate models • Evaluate capacity of climate models to reproduce observed extremes

  6. Highlights • UK Climate Impacts Program • Scenarios Gateway page • Guidance on scenario use and development • Access to maps and datasets • Canada – Climate Change Scenarios Network (CCSN) • Network of researchers providing scenarios and advice to the impacts and adaptation community • Provision of CRCM output • On-line automated statistical downscaling tool, based on SDSM

  7. General Characteristics • Global and regional changes in mean values • Annual, seasonal, (monthly) • Useful for determining broad changes, e.g. • Growing season length • Moisture availability • Broad vulnerability to pests, disease • Limitations for determining crop dynamics, pest hazard cycles

  8. Challenges • Extracting/Inferring climate change scenarios from the impact assessments • Requirements for developing climate change scenarios at national levels • Data requirements/demands • Technical requirements (RCMs, downscaling tools/methodologies)

  9. Recommendations • Collaborate with weather generator developers from the Southeast Climate Consortium (SECC) and develop joint efforts for downscaling climate change scenarios • Develop downscaled climate change scenarios for the different WMO regions, 0.5 degree (this may require proposal and development of a separate project) • Identify agroclimatic indices and extremes important to each region/sub-region • Develop agroclimatic fields at regional/sub-regional levels • Develop risk maps at regional/sub-regional levels

  10. JJA Number of days Tmax≥30˚C(2040-2069) CGCM1 HadCM3

  11. Suggestions ? ? ?

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