Climate Change Scenarios for Agriculture

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 • Review some of the climate change scenarios being developed for agricultural impact and adaptation assessments • Present AAFC research on climate variability and change in a Canadian context

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 • Effects of Climate Change on Agriculture, Land Resources, Water Resources and Biodiversity (2008) Climate Change Science Program • 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)

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. AAFC Climate Change Scenarios Research • Background on climate change and scenario development • AAFC weather generator • Findings on agroclimatic indices and extremes • Links to crop response

9. Downscaling • Output from the nearest GCM grid point used at times to evaluate impacts of future climate • However, downscaling is required to construct realistic regional or local scenarios from GCM outputs • There are two main approaches to downscaling - dynamical and statistical

10. Dynamical approaches • High-resolution atmosphere-only GCMs • Nested regional climate models (RCMs) • Formulated using physical principles • Computationally expensive • Parameterization schemes for processes at sub-grid scales may be operating outside the range for which they were designed

11. Statistical approaches • Regression-type models. • Weather generators. • Weather classification. • Analogue methods. • Computationally inexpensive; function at finer scales than dynamical methods; applicable to parameters that cannot be directly obtained from RCM outputs • Require observational data at the desired scale for a long enough period; assume that the derived cross-scale relationships remain valid in a future climate; cannot effectively accommodate regional feedbacks and can lack coherency among multiple climate variables under some approaches.

12. AAFC-WG • An unconditional weather generator • Richardson-type weather generator • Precipitation occurrence is simulated by a two state second order Markov chain • Precipitation amounts, temperatures and radiation are simulated by empirical distributions of the observed data

13. AAFC-WG (continued) • Validated for diverse Canadian climates • Has been compared with other stochastic weather generators – LARS-WG • Evaluated for extreme daily values • Developed schemes for perturbing weather generator parameters based on GCM-simulated changes in the statistics of daily climate variables

14. Climate Change Scenarios Data Two sets of daily climate scenarios data • CGCM1 IS92a GHG+A and HadCM3 A2. • On 0.5°grids for south of 60°N • For the future time period of 2040-2069 • Values of daily Tmax, Tmin, P and Rad • Generated by AAFC-WG

15. Scenarios data for ecodistricts • Two data sets for CGCM1 IS92a (GHG+A) and HadCM3 A2. • Developed with the “delta” method. • For the future period of 2040-2069. • Daily Tmax, Tmin, precipitation and Rad. • Centroids of ecodistricts where daily weather data are available at neighbouring stations.

16. Some evaluations using the scenarios data • Agroclimatic indices (e.g. frost-free days, last frost day in spring and first frost day in fall, GDD, EGDD, CHU, precipitation deficit) • Annual and growing-season extreme values of daily Tmax, Tmin and precipitation, their 10yr, 20yr and 50yr return values • Relative changes to 1961-1990 baseline climate

17. Last Frost in Spring(2040-2069) CGCM1 HadCM3

18. Last Frost in Spring(Changes) CGCM1 HadCM3

19. First Frost in Fall(2040-2069) CGCM1 HadCM3

20. First Frost in Fall(Changes) CGCM1 HadCM3

21. Frost-Free Days(Changes) CGCM1 HadCM3

22. Precipitation Deficit/Surplus(2040-2069) CGCM1 HadCM3

23. Precipitation Deficit/Surplus(Changes) CGCM1 HadCM3

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

25. JJA Number of days Tmax≥30˚C(Changes) CGCM1 HadCM3

26. DJF Number of days Tmin≤-20˚C(2040-2069) CGCM1 HadCM3

27. DJF Number of days Tmin≤-20˚C(Changes) CGCM1 HadCM3

28. Corn yields increase about 0.6 t ha-1 for each increase of 100 CHU CHU versus grain corn yields in eastern Canada

29. CHU versus soybeans yields in eastern Canada Soybean yields increase about 0.13 t ha-1 for each increase of 100 CHU

30. Barley yields versus Effective Growing Degree-Days above 5ºC (EGDD) Increasing EGDD by 400 units reduces yield of 6-row and 2-row barley about 0.6 and 0.4 t ha-1, respectively

31. Barley yields versus water deficit (PE – P)

32. Average corn yields vs CHU – USA Locations 1 = Illinois 2 = Nebraska 3 = Indiana 4 = Iowa 5 = Ohio 6 = Missouri i = irrigated (based on average yield of top 10 hybrids in field trials, 4 to 8 yrs data, 1994-2001) Agriculture and Agri-Food Canada

33. Corn yields vs Water Deficits – USA Locations 1 = Illinois 2 = Nebraska 3 = Indiana 4 = Iowa 5 = Ohio 6 = Missouri (based on average yield of top 10 hybrids in field trials, 4 to 8 yrs data, 1994-2001) Agriculture and Agri-Food Canada

34. Planned scenarios • New data sets for CGCM3 (A2, A1B, B1) and HadCM3 (A1B, B1) • For the future period of 2040-2069 • Gridded and/or ecodistrict scales • Continuous 2000-2099 data for a range of stations • Daily Tmax, Tmin, precipitation and Rad.

35. Summary • There is a need for a shift from scenarios based on annual, seasonal, or monthly climate values, to daily ones for agricultural impact assessments. • AAFC-WG was suitable for the development of daily climate scenarios, and scenarios of extremes. • Earlier last frost in spring and later first frost in fall with a longer growing season are projected. • There would be an increase in crop heat units under climate change. • Larger precipitation deficits can be expected, especially in the Prairies. • An increase in extremely hot days in summer is foreseen. • Increased crop heat units will likely result in increased production of corn and soybeans, but decreased barley yields. • Crop response may be more sensitive to extremes. We will be carrying out more studies on the impacts of climate extremes. We will make use of crop modelling for this purpose.

36. THANK YOU!