Past and Projected Changes in Continental-Scale Agro-Climate Indices

1. Past and Projected Changes in Continental-Scale Agro-Climate Indices Adam Terando NC Cooperative Research Unit North Carolina State University 2009 NPN RCN Meeting

2. Motivating Questions • Is the late 20th century warming found in the surface temperature record also observable in alternative climate measures that are critical to agricultural production and phenological observations in North America? • Do Global Climate Models (GCMs) have skill in hindcasting the observed trends? • What changes do GCMs predict for the future?

3. Global Mean Temperature over Land & Ocean Global Scale National Climatic Data Center: 2006

4. BUT….. An increase in mean global surface temperature will not necessarily be reflected in the same manner for other manifestations of the climate system over the same time period and at different spatial scales.

5. Meehl et al. 2000

6. A Temperature Example Heat Stress Frost/Freeze Crop Growth

7. Agro-Climate Indices • Annual Frost Days (tmin < 0 oC) • Growing Degree Days (thermal time) for Corn (10 < tavg < 30 oC) • Strong correlation with crop growth • Heat-Stress Index (tmax > 30 oC)

8. US and Canadian Long-term Historical Climate Networks

9. -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 1880 1900 1920 1940 1956 1956 1956 1960 1976 1976 1975 1980 2000 2000 2005 2005 2005 Trend Time Periods • 1956 – 2005: Good data coverage • Switch in 1970s • Warming signal detected then on global scale. • Also coincides with phase shift in North American tele-connections (i.e. PDO, NAO) • Most recent data

10. SPATIAL PATTERNS

11. Frost Trends (1956 – 2005) Slope (Days/Year) < -0.5 > 0.5 -1 0 1

12. Growing Degree Day Trends (1956 – 2005) Slope (Days/Year) > 5 < -5 7 0 -7

13. Heat Stress Index Trends (1956 – 2005) Slope (Degree Days Per Year) 10 > 2.5 < -2.5 0 -10

14. Percent Stations with Significant Trends

16. a) b) c) • Trends fairly consistent through time

17. GCM Results

18. GCM Data • 17 GCMs available from Lawrence Livermore National Laboratory • Models used in IPCC AR4 • Fewer years and model runs available for daily data than for monthly data (requires more storage!) • Typically 40 years available for 20th century (1961 – 2000), and two 20 years periods for 21st Century (2045 – 2065 and 2081 – 2100)

19. Questions • Do GCMs have skill in simulating past changes in agro-climate indices? • What future changes do GCMs predict? • Is the (projected) signal strong with respect to the model noise?

20. Evaluating GCM Skill

21. r = 0.52 r = 0.17 GCM Arithmetic Mean Observations SLPobs = -0.22 SLPgcm = -0.21 SLPobs = 0.50 SLPgcm = 3.42 GCM Results r = 0.03 SLPobs = 0.04 SLPgcm = 1.59 Frost Days GDD • Poor performance for GDD and HSI evident in trend lines • Good agreement with frost days HSI

23. Correlation Coefficient RMS Error Model Result Observation or ‘Perfect’ Model Standard Deviation Taylor Diagram Taylor 2001

24. Model Weighting GCMs “perfect” model Schneider et al. 2007

25. Correlation Coefficient Correlation Coefficient Standard Deviation Thermal Time Frost Days Centered RMS Difference Standard Deviation Centered RMS Difference Heat Stress Index

26. 16 Correlation Coefficient Heat Stress Days Heat Stress Index Correlation Coefficient a) b) Standard Deviation Standard Deviation Centered RMS Difference Centered RMS Difference Year Year c) d)

27. Minimum Temperature Maximum Temperature Correlation Negative Standard Deviations Positive Standard Deviations

28. bccr-bcm2.0 echam5-MPI miroc3.2 mri-cgcm2.3.2 observations

29. Projections

30. A2 Scenario IPCC Emission Scenarios

31. GCM Arithmetic Mean Observations 2046-2065 Weighted Mean 2081-2100 Weighted Mean GCM Results Thermal Time Frost Days Heat Stress Index

32. Projected changes large relative to model errors for 20th century • Largest uncertainties (model spread) around HSI projections

33. Conclusions • General signal agreement between Tavg and agro-climate indices. • Strong increase in Thermal Time and decrease in Frost Days that is not seen in HSI. • Still difficult for GCMs to model variables requiring high temporal resolution. • Ensemble mean has greater skill than indiviudal GCMs • Large changes in agro-climate indices predicted by GCMs for A2 scenario.