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Targeting Predictability & Prediction Research for Applications

Targeting Predictability & Prediction Research for Applications. Lisa Goddard Stephen E. Zebiak International Research Institute for Climate & Society The Earth Institute of Columbia University. BIG PICTURE.

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Targeting Predictability & Prediction Research for Applications

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  1. Targeting Predictability & Prediction Research for Applications Lisa Goddard Stephen E. Zebiak International Research Institute for Climate & Society The Earth Institute of Columbia University 1st Annual US CLIVAR Summit

  2. BIG PICTURE Provide climate information/predictions that is consistent with the needs of users and decision-makers. Understand how components and processes of the climate system fit together. Develop/identify/apply appropriate tools to predict those aspects of variability that are deemed necessary to predict, where and when possible. 1st Annual US CLIVAR Summit

  3. OUTLINE • Identifying and characterizing predictability • Improving prediction capabilities • Facilitating communication between research and decision-makers 1st Annual US CLIVAR Summit

  4. Identifying & Characterizing PredictabilityWhat is the level of predictability? Is the variability only conditionally predictable? • What is the limit of predictability? What is the limit of prediction-ability? • Can we identify the cause(s) for the difference between the two? • For USA, predictability vs. prediction-ability would suggest improvements in ENSO forecasts would, at the least, extend lead time 1st Annual US CLIVAR Summit

  5. Conditional SkillMore confident forecasts over USA under ENSO conditions Ranked Probability Skill Scoresfor 3-category precipitation frommulti-model AGCM simulations. Period = 1950-1998 1st Annual US CLIVAR Summit

  6. Identifying & Characterizing PredictabilityWhat is the level of predictability? Is the variability only conditionally predictable? • Focus on information users want • Characteristics of weather w/i climate • Timing – e.g. start of rainy season • Persistence – e.g. drought • Decadal-scale climate prediction • Greater spatial detail in predictions • Seek solid foundation in understanding the underlying phenomenon/process 1st Annual US CLIVAR Summit

  7. Need Predictability & Prediction Research on Decadal Variability Western North America : DJF Temperature “Amazon” : Annual-Mean Temperature 1st Annual US CLIVAR Summit

  8. II. Improving Prediction CapabilitiesWant reliability. Increase sharpness to the extent possible. • Need “good” models and “good” assimilation systems • Make the most of existing tools • Multi-model ensembling • Statistical correction (i.e. MOS) 1st Annual US CLIVAR Summit

  9. Forecast Reliability (3-category forecasts) Forecasts should “mean what they say”. (3-category forecasts) 1st Annual US CLIVAR Summit

  10. II. Improving Prediction CapabilitiesWant reliability. Increase sharpness to the extent possible. • Need “better” models and “better” assimilation systems • Make the most of existing tools • Multi-model ensembling • Statistical correction (i.e. MOS) 1st Annual US CLIVAR Summit

  11. 1-Tier vs. 2-Tier GCMs Precipitation Skill : USA 1983-2002 – 7 member ensembles 2-Tier: AMIP 2-Tier: Retro. 1-Tier (CGCM) 1st Annual US CLIVAR Summit

  12. Combining models reduces deficiencies of individual models Benefit of Using Multiple Models RPSS for 2m Temperature (JFM 1950-1995) 1st Annual US CLIVAR Summit

  13. II. Improving Prediction CapabilitiesWant reliability. Increase sharpness to the extent possible. • Higher resolution, in space & time • Targeting new forecast variables • Streamflow • NDVI • Dry spells • Heating Degree Days 1st Annual US CLIVAR Summit

  14. Higher resolution information is not necessarily more skillful interannually Rainfall correlations between simulations & observationsFMA 1971-2000 2.8 °x2.8° resolution 60 km resolution 1st Annual US CLIVAR Summit

  15. Regional Model Does Better at Weather w/i Climate Corn Yield Prediction Usinga) seasonal mean rainfall, b) weather index 1st Annual US CLIVAR Summit

  16. Problem area User(s) Requirement Application 1) Areas of high Rift Valley Fever (RVF) outbreak risk Red Sea Livestock Trade Commission Predict RVF risk areas 3-6 months in advance Identify and treat RVF outbreaks before regional trade barriers are imposed 2) Livestock fodder availability Pastoral communities in northern Kenya and southern Ethiopia Narrow the confidence interval of the current Livestock Early Warning System (LEWS) 90 day fodder outlook using a seasonal forecast Provide improved 90 day early warning to nomadic pastoralists and sedentary agro-pastoralists of expected fodder conditions 3) Livestock fodder availability Organization of African Union/Inter-African Bureau of Animal Resources (OAU/IBAR) Provide 3-6 month early warning of major regional fodder shortages Support IBAR livestock purchase programs 4) Pastoralist livelihood system stress USAID, other donors and international emergency assistance organizations Simulate climate shock impacts on pastoralist livelihood systems and food security Contingency and operational assistance planning Greater Horn of Africa Project Tailored Products 1st Annual US CLIVAR Summit

  17. Statistical Downscaling to NDVI • Using a GCM with Sept SST to • predict December vegetation • across East Africa 1982-1998 • Spatial variations in skill may reflect • variations in climate predictability • variations in climate-NDVI coupling • Hypotheses to explore using RCMs. Units are correlation skill Contours are elevation Corrected high resolution NDVI provide by USGS Time series of area-average predicted NDVI over NE Kenya (r=0.76) 1st Annual US CLIVAR Summit

  18. III. Facilitating Communication Between Research & Decision-Makers • Involve users/decision-makers in process to target their needs and decisions • CLIVAR’s best point of contact with “users” is through boundary organizations, such as RISAs, IRI, etc. • What are most appropriate mechanisms for interaction? 1st Annual US CLIVAR Summit

  19. NGOs Line Ministries Private sector Organizations structure, culture and policies Climate risk knowledge management - diffusion Multiplier effect US & INTERNATIONAL DONORS International Development targets -measurement User demands and pressure from stakeholders (including donors) Resources human, financial, technological Formal research, evidence creation, underpinning activities – real world demonstration The Climate and Society Report Tacit knowledge Media networks Multiple implementers 1st Annual US CLIVAR Summit

  20. Epidemic control MoH, District authorities, NGOs, regional and international orgs, climate info NMS, DMC, FEWS Rainfall, SSTs and GCMs predictive of malaria anomalies MDG Goals 4, Maternal mort 5, Child mort 6, M/HIV/TB International and national targets require improved malaria control RBM, MDGs Resources Development of malaria epidemic tools Data library link to WHO global atlas Climate = driving force of epidemics MALARIA in AFRICA Implementers MoH district, national, regional, international institutions donors 1st Annual US CLIVAR Summit

  21. BIG PICTURE Provide climate information/predictions that is consistent with the needs of users and decision-makers. Understand how components and processes of the climate system fit together. Develop/identify/apply appropriate tools to predict those aspects of variability that are deemed necessary to predict, where and when possible. 1st Annual US CLIVAR Summit

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