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Models as bio-indicators – why we need good climate records

Models as bio-indicators – why we need good climate records. Holger Meinke and many other colleagues CWE, Plant Sciences Group, Wageningen University, Netherlands APSRU, Australia. Converting the vagrancies of weather and climate into real options for risk management ….

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Models as bio-indicators – why we need good climate records

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  1. Models as bio-indicators – why we need good climate records Holger Meinke and many other colleaguesCWE, Plant Sciences Group, Wageningen University, NetherlandsAPSRU, Australia

  2. Converting the vagrancies of weather and climate into real options for risk management … … is a long and treacherous task that requires science without disciplinary dominance, good partnerships and lots of patients. Uncertainty = randomness with unknowable probabilities Risk = randomness with knowable probabilities Real Options = values flexible & adaptive mgt in response to uncertainty (in contrast to discounted cash flow analysis) Knight, F.H., 1921

  3. Background and Motivation • The 20th centurywas the century of analysis based on new discoveries and exploring biological systems in ever increasing detail (from discovery of DNA to mapping of the human genome), creating new disciplines in the process • The 21st centuryis rapidly becoming the century of synthesis with much greater emphasis on holistic approaches and creating new insights at the interfaces of disciplines (transdisciplinary)

  4. Analysis Action Climate factors (eg rainfall, MSLP, SST) Impacts Impacts and adaptation Integration with other issues – policy, risk management Climate risk mgt mainstreamed, increased ‘climate knowledge’

  5. Vulnerability Adaptive Capacity Two sides of the same coin HARDSHIP OPPORTUNITY CRISIS Without adaptation With adaptation Source: IRI

  6. Example of an application • Simulation models are ideal diagnostic tool for identifying broadly based seasonal to annual climatic drivers • Hence, we used simulated wheat yield as an integrative quality of rainfall over a long period • Contrary to traditional climatic analyses we started with impacts to investigate processes (model as a bio-indicator) Potgieter, A.B, Hammer, G.L., Meinke, H., Stone, R.C. and Goddard, L., 2005. Spatial variability in impact on Australian wheat yield reveals three putative types of El Niño. Journal of Climate, 18: 1566-1574.

  7. Probability of exceeding longterm median wheat yields in El Niño years for every wheat-producing district in Australia simulated with a process-based model that uses daily temperature and rainfall records as input and has ‘memory’ at a range of time scales. Now conduct a multivariate analysis on these simulated district wheat yields to reveal spatial impact patterns Dual purpose application

  8. 3.5 3.0 2.5 2.0 Linkage Distance 1.5 1.0 Group 3 Group 2 Group 1 0.5 0.0 1946 1997 1941 1972 1977 1925 1993 1969 1992 1951 1991 1987 1953 1911 1905 1982 1914 1994 1965 1957 1919 2002 1940 1902 Multivariate Analysis Footprint 1 Footprint 2 Footprint 3

  9. Average standardised shire wheat yield relative to all years Footprint 1 (6 years) Footprint 2 (9 years) Footprint 3 9 years

  10. Average standardised shire rainfall relative to all years Footprint 1 (6 years) Footprint 2 (9 years) Footprint 3 9 years

  11. FP1 FP2 FP3

  12. SPCZ FP1 FP2 FP3

  13. Footprints of El Niño • The evolution of the EN events (timing of onset and locations of the major SST and MSLP anomalies) is highly variable but can be clustered into 3 distinct groups. • Over Australia MSLP anomalies were shifted southward affecting the latitude of the sub-tropical ridge, which has significant association with rainfall variability. • The shift in tilt of the line of separation between warm and cool SST pools from years in FP1 and FP2 to those in FP3 suggests linkage to the location of the South Pacific Convergence Zone, which has significant association with decadal variability in rainfall.

  14. Results • The results indicate that climate system drivers that are responsible for variation in EN events and decadal rainfall patterns. • The associations suggest that variability in impact is most likely forced by differences in the temporal evolution and spatial extent of the ocean/atmosphere patterns. Features outside the tropical Pacific are also contributing. • Plausible mechanisms need to be investigated further by linking of crop models to GCM (lead to better targeted predictive agricultural systems).

  15. We need good, longterm, (daily) surface records … … but NOT to analyse in ever greater detail quantities that decision makers have no control over (e.g. rainfall). We really need them as input into biophysical decision models that provide REAL OPTIONS for decision making.

  16. Multiple dimensions Biophysical Towards ‘Real Options’ for policy: Exposure vs Coping Capacity high vulnerability moderate vulnerability low vulnerability Meinke et al., 2006. Actionable climate knowledge – from analysis to synthesis. Climate Research, 33: 101-110.

  17. What usually happens - A case of market failure Sector specific demand for climate-related forecasts and risk assessments Local, longterm weather / climate data Supply of climate variables in ever increasing detail Dynamic climate model

  18. Spatial-temporal impact on BP quantity Bio-physical model Local, longterm weather / climate data Sector specific demand for climate-related forecasts and risk assessments Dynamic climate model Statistical model categorising impacts (PC or pattern analysis) Global patterns of fundamental climate drivers Spatial-temporally coherent impacts Statistical model clustering global climate indicators

  19. Spatial-temporal impact on BP quantity Bio-physical model Local, longterm weather / climate data Adaptive capacity creation via provision of REAL OPTIONS Dynamic climate model Statistical model categorising impacts (PC or pattern analysis) Global patterns of fundamental climate drivers Spatial-temporally coherent impacts Statistical model clustering global climate indicators

  20. Beyond Impact - the business case for Action • Adaptation and preparedness have emerged as THE biggest issues for a post-Kyoto world – not many know how to do it. • The excellent SUPPLY of scientific information and insights will remain without impact (diffuse and unfocused) in the absence of clear user DEMAND for climate services. • Policy makers AND practitioners need access to relevant information for informed discussions or debates. • We need to become more transdisciplinary and problem oriented in our approaches to science – without disciplinary dominance. • We need good modelling tools for all sectors, not just climate.

  21. References Howden et al. 2007. Adapting agriculture to climate change. PNAS, 104(5), 19691–19696 Lo et al. 2007. Probabilistic forecasts of the onset of the North Australian wet season. MWR, 135, 3506-3520. Maia et al. 2007. Inferential, non-parametric statistics to assess quality of probabilistic forecast systems. MWR, 135, 351-362. Meinke et al. 2006. Actionable climate knowledge – from analysis to synthesis. Climate Research, 33: 101-110. Open access at http://www.int-res.com/articles/cr_oa/c033p101.pdf . Meinke, H. et al. 2007. Climate predictions for better agricultural risk management. Aust. J. Agric. Res., 58, 935-938. Nelson, R. et al. 2007. From rainfall to farm incomes - transforming policy advice for managing climate risk in Australia. Aust. J. Agric. Res., 58, 1004-1012. Potgieter, A.B, Hammer, G.L., Meinke, H., Stone, R.C. and Goddard, L., 2005. Spatial variability in impact on Australian wheat yield reveals three putative types of El Niño. Journal of Climate, 18: 1566-1574.

  22. Methods • ENSO classification – combined ocean (Niñ0 3.4 SST - Trenberth, 1997) and atmosphere (SOI - Ropelewski & Jones , 1987) classification. • Multivariate analysis on simulated shire wheat yields (Potgieter et al. , 2002) • Align standardised weighted wheat yields with standardised shire rainfall • Mapped 3-monthly SST & MSLP (Smith & Reynolds, 2003)

  23. Year SOI SST Year SOI SST Year SOI SST ?? 1901 1935 1969 ?? 1902 1936 1970 Using the SST time series for Niño 3.4, a year was classified as EN if the 5-month running mean was ≥ 0.5 for six or more months between April and December (Trenberth, 1997). Using the SOI time series, a year was classified as EN if the 3-month running mean was ≤ –5.5 for six or more months between April and December (Ropelewski and Jones, 1987). 1903 1937 1971 ?? ?? 1904 1938 1972 ?? ?? 1905 1939 1973 ?? ?? 1906 1940 1974 ?? ?? 1907 1941 1975 1908 1942 1976 ?? ?? 1909 1943 1977 1910 1944 1978 ?? 1911 1945 1979 ?? 1912 1946 1980 1913 1947 1981 ?? ?? ?? 1914 1948 1982 1915 1949 1983 1916 1950 1984 ?? 1917 1951 1985 1918 1952 1986 ?? ?? ?? ?? ?? 1919 1953 1987 1920 1954 1988 1921 1955 1989 1922 1956 1990 ?? ?? ?? 1923 1957 1991 ?? ?? 1924 1958 1992 ?? ?? ?? 1925 1959 1993 ?? ?? 1926 1960 1994 1927 1961 1995 1928 1962 1996 ?? ?? 1929 1963 1997 1930 1964 1998 ?? ?? 1931 1965 1999 1932 1966 2000 1933 1967 2001 ?? ?? 1934 1968 2002

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