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Asmerom F. Beraki Cobus Olivier Long-Range Forecasting (LRF) Group SAWS Willem Landman NRE-CSIR

Introduction to Long-Range Forecasting. Asmerom F. Beraki Cobus Olivier Long-Range Forecasting (LRF) Group SAWS Willem Landman NRE-CSIR. Training Material. Purpose of Presentation. Introduce long-range forecasting principles and their customized utilization in operational environments;

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Asmerom F. Beraki Cobus Olivier Long-Range Forecasting (LRF) Group SAWS Willem Landman NRE-CSIR

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  1. Introduction to Long-Range Forecasting Asmerom F. Beraki Cobus Olivier Long-Range Forecasting (LRF) Group SAWS Willem Landman NRE-CSIR Training Material

  2. Purpose of Presentation • Introduce long-range forecasting principles and their customized utilization in operational environments; • the contents of this material serve solely for the purpose of the AMESD project training needs; it shouldn’t be used for other purposes without the prior permission of the respective authors. Document Reference: RES-LRF-AMESD-001-2010-09-13 Document Template Reference: TQM-PRE-001.1 Date of last revision: 26 May 2010

  3. PART 1 Out lines • Introduction • Numerical Weather Prediction on Longer Time-Scales • Atmospheric Circulation and Slowly evolving boundary forcing • SAWS experience in the area of climate modelling (operations and research) Document Reference: RES-LRF-AMESD-001-2010-09-13 Document Template Reference: TQM-PRE-001.1 Date of last revision: 26 May 2010

  4. introduction • Global Climate models (1-tier and 2-tier systems): • Mathematical procedure to simulate the interactions of the atmosphere, oceans, land surface, and ice • The procedure involves: • Dynamic processes • Physical parameterization • Numerical approximations • Downscaling Issues • Why is it necessary? • Dynamical DS (e.g., nested climate models, stretched grid models) • Empirical / Statistical DS (e.g., multiple regression, Canonical Correlation Analysis …. ) altitude Document Reference: RES-LRF-AMESD-001-2010-09-13 Document Template Reference: TQM-PRE-001.1 Date of last revision: 26 May 2010

  5. introduction • Uncertainties in climate models (sources) • Initial states: from the same set of initial states, different models typically produce a different set of forecast outcomes • Our lack of understanding and imperfections in model formulations, • boundary forcing (2-tier) • How uncertainties are represented in forecasts? • Ensemble prediction system and Multi-Model System • EPS is collection of predictions which collectively “explore” the possible future outcomes, given the uncertainties inherent in the forecast process Document Reference: RES-LRF-AMESD-001-2010-09-13 Document Template Reference: TQM-PRE-001.1 Date of last revision: 26 May 2010

  6. introduction • The ensemble spread is increased relative to the individual model ensembles. Thus the observed outcome more frequently falls within the range of forecast solutions provided by the ensemble. • The Multiple-model provides a filter for the more skilful individual model (the best model will vary with season/variable/region). Thus the strengths of the individual models are exploited, improving capabilities for global climate prediction. • Benefits derive mainly from the use of additional models, but also from the increased ensemble size Document Reference: RES-LRF-AMESD-001-2010-09-13 Document Template Reference: TQM-PRE-001.1 Date of last revision: 26 May 2010

  7. Numerical Weather Prediction on Longer Time-Scales • Limitations • Great progress has been made to predict the day-to-day state of the atmosphere (e.g., frontal movement, winds, pressure) • However, day-to-day fluctuations in weather are not predictable beyond two weeks • Beyond that time, errors in the data defining the state of the atmosphere at the start of a forecast period grow and overwhelm valid forecast information • This so called “chaotic” behaviour is an inherent property of the atmosphere Document Reference: RES-LRF-AMESD-001-2010-09-13 Document Template Reference: TQM-PRE-001.1 Date of last revision: 26 May 2010

  8. Numerical Weather Prediction on Longer Time-Scales Maturity of NWP on shorter time-scales • Skill improvements are the result of improvements: • dynamics and physics of numerical models • observational network • computational infrastructure - realization ensemble prediction system • understanding to atmosphere-ocean-land processes and their interactions (more pronounced on longer time scales). Courtesy of ECMWF Document Reference: RES-LRF-AMESD-001-2010-09-13 Document Template Reference: TQM-PRE-001.1 Date of last revision: 26 May 2010

  9. Numerical Weather Prediction on Longer Time-Scales Maturity of NWP on shorter time-scales Forecast error over the European domain (500 hPa geopotential heights) 140 120 Progress! Forecasterrors made by a 1998 model after 5 days, are similar to errors made after 2 days by a 1975 model. 100 80 error in gpm 60 1998 1990 1980 40 1975 persistence climatology 20 0 Training Material 0 1 2 3 4 5 6 7 8 9 10 lead-time (days) Document Reference: RES-LRF-AMESD-001-2010-09-13 Document Template Reference: TQM-PRE-001.1 Date of last revision: 26 May 2010

  10. How then possible predicting climate anomalies on longer time-scales? • Bases of climate modelling: • The presence of sufficient physical basis for predicting the mean state of the atmosphere on longer time-scales is documented in the literature (e.g., Shukla, 1981; Shukla and Gutzler, 1983; Mason, et al., 1999) • The source of predictability revolves around: • improving numerical models, initial conditions and the parameterization of physical processes • By adding slowly evolving boundary conditions to the system most notably SSTs (i.e., El Niño and La Niña) and its influence on the atmospheric circulation (more pronounced at seasonal time-scale) Sea-surface temperature (SST) anomalies of September 1997 (El Niño of 1997/98) Anomaly: departure from the mean or average Sea-surface temperature (SST) anomalies of November 1988 (La Niña of 1988/89) Document Reference: RES-LRF-AMESD-001-2010-09-13 Document Template Reference: TQM-PRE-001.1 Date of last revision: 26 May 2010

  11. How then possible predicting climate anomalies on longer time-scales? • Bases of climate modelling : • In the context of seasonal forecasting, the forecast period, lead-time and persistence issues have a significant importance as far as the quality of a particular forecast assessment is concerned. • What is desired is best quality forecast for permissible longer lead-time to address complex climate application needs climatology: averagetaken over a long time; the forecastisthat the average value willhappen. persistence: ‘today’sweatheriswhatwillhappentomorrow’. Document Reference: RES-LRF-AMESD-001-2010-09-13 Document Template Reference: TQM-PRE-001.1 Date of last revision: 26 May 2010

  12. DynamicalForecasts: MonthlyForecasts Daily Scores over NorthernHemisphere + Monthly running mean Scores Courtesy of Meteo France Document Reference: RES-LRF-AMESD-001-2010-09-13 Document Template Reference: TQM-PRE-001.1 Date of last revision: 26 May 2010

  13. DynamicalForecasts: SeasonalForecasts Daily Scores over NorthernHemisphere + Seasonal running mean Scores Courtesy of Meteo France Document Reference: RES-LRF-AMESD-001-2010-09-13 Document Template Reference: TQM-PRE-001.1 Date of last revision: 26 May 2010

  14. DynamicalForecasts: SeasonalForecasts Daily Scores over NorthernHemisphere + Ensemble forecast, Seasonal running mean and SST forecast Courtesy of Meteo France Document Reference: RES-LRF-AMESD-001-2010-09-13 Document Template Reference: TQM-PRE-001.1 Date of last revision: 26 May 2010

  15. Atmospheric Circulation and Slowly evolving boundary forcing • Atmospheric Circulation is large-scale movement of mass and energy and triggered by thermal gradient • Hadley cell: circular motion of air masses toward poles at tropopause (trough; ITCZ) and toward equator at the surface (trade winds) characterized by rising unstable warm and moist air and subsiding dry air (ridge). • Ferrell cell: eddy-driven mid-latitude circulation though not closed cell (causes upper and low level westerlies) with no strong source heat, cold sink . The course of westerlies is easily overridden by moving weather system • Polar cell: circular motion driven by thermal gradient; Polar easterlies are the result of this cell and Corolis effect. Document Reference: RES-LRF-AMESD-001-2010-09-13 Document Template Reference: TQM-PRE-001.1 Date of last revision: 26 May 2010

  16. Atmospheric Circulation and Slowly evolving boundary forcing • Walker circulation: • Many overturning equatorial zonal cells are also present (such as equatorial Africa, Central and South America and Indonesian region) • Of which equatorial Pacific is most dominant and referred to as Walker Circulation • caused by longitudinal SST variations as a consequence of wind-driven Ocean currents. • Produce zonally asymmetric atmospheric circulation and in some regions may dominate the Hadley Cell • East-west pressure gradient mainly associated with WC undergoes an irregular interannual variation • This global scale variation in pressure and consequential changes in wind, temperature and precipitation patterns named Southern oscillation by Walker During La Niña; After Webster and Chang, 1988 courtesy of IPCC AR4 Document Reference: RES-LRF-AMESD-001-2010-09-13 Document Template Reference: TQM-PRE-001.1 Date of last revision: 26 May 2010

  17. Atmospheric Circulation and Slowly evolving boundary forcing • Source of Predictability: • Slowly evolving boundary forcings notably equatorial Pacific SST gradient are therefore the major players in modulating the meridional (Hadley Cell) and Zonal (Walker) overturning atmospheric circulations • Extreme phases of ENSO (major coupled ocean–atmosphere phenomenon) represents the single most prominent mode of climate variability at seasonal and interannual time scales. • Equatorial Atlantic Ocean Dipole (AOD), Equatorial Indian Ocean Dipole (IOD), Land surface forcing (such as soil moisture…) also among contributors though relatively less understood • In the Southern African context, these SST gradients are believed to modulate the relative annual position of the Inter-tropical convergence zone (ITCZ), the South Atlantic anticyclone, and the midlatitudewesterlies albeit not extensively investigated or well understood Document Reference: RES-LRF-AMESD-001-2010-09-13 Document Template Reference: TQM-PRE-001.1 Date of last revision: 26 May 2010

  18. SAWS experience in the area of climate modelling (operations and research) Document Reference: RES-LRF-AMESD-001-2010-09-13 Document Template Reference: TQM-PRE-001.1 Date of last revision: 26 May 2010

  19. SAWS experience in the area of climate modelling (operations and research) SAWS Ensemble Prediction system • Global Precip. and Temp. Forecasts for 3 seasons (up to 5 months ahead) • Uninitialized System • Forced with persisted and forecast SST scenarios • 12 ensemble members • Provides operational probabilistic forecast for different regions • Feeds information to the Multi-Model System (SAWS) • Forcing regional climate models (RecCM3) http://old.weathersa.co.za/LONGTERM/lrf.html Document Reference: RES-LRF-AMESD-001-2010-09-13 Document Template Reference: TQM-PRE-001.1 Date of last revision: 26 May 2010

  20. SAWS experience in the area of climate modelling (operations and research) SAWS as GPC for LRF • Fixed production cycles and time of issuance • Provision of limited set of products • Provision of verifications as per the WMO Standard “Standardized Verification System for Long-Range Forecast (SVS-LRF) • Provide up-to-date information on methodology used by the GPC • Accessibility of products http://old.weathersa.co.za/cycloneWeb/LONGTERM/SVSLRF/SAWS_SVSLRF.htm http://www.bom.gov.au/cgi-bin/climate/wmo.cgi Document Reference: RES-LRF-AMESD-001-2010-09-13 Document Template Reference: TQM-PRE-001.1 Date of last revision: 26 May 2010

  21. SAWS experience in the area of climate modelling (operations and research) New Seasonal Forecasting System • enhancements relative to the existing operational forecasting system: • the model knows the actual state of the atmosphere during initialization • SST scenarios with better description of uncertainties • Land surface model is initialized with realistic soil moisture • 1) Description of initial conditions • The atmospheric initial conditions are suitably transformed (in a manner that respects numerical stability) – source NCEP/DOE (Kanamitsu et al., 2002) that involves: • Horizontal interpolation • vertical interpolation based on the vertical integration of the hydrostatic equation with some adjustments that maintains geostrophic balance and mass conservation • Grid to spectral transformation (T42L19) • Uncertainties are accounted by taking 10 consecutive daily NCEP atmospheric states back from the forecast date in each year (i.e., October 26 – November 4). Document Reference: RES-LRF-AMESD-001-2010-09-13 Document Template Reference: TQM-PRE-001.1 Date of last revision: 26 May 2010

  22. SAWS experience in the area of climate modelling (operations and research) New Seasonal Forecasting System • Description of boundary Conditions • The NCEP CFS SST ensemble forecasts background error that accounts different lead-times is identified from the dominant mode of Principal Component Analysis (PCA) Document Reference: RES-LRF-AMESD-001-2010-09-13 Document Template Reference: TQM-PRE-001.1 Date of last revision: 26 May 2010

  23. SAWS experience in the area of climate modelling (operations and research) New Seasonal Forecasting System The skill of ECHAM 4.5 AGCM in predicting austral summer total precipitation qualitatively; model simulation (left) and CMAP-CPC (right). The skill of ECHAM 4.5 AGCM in predicting total precipitation probabilistically; ROC area (left) below-normal and (right) above-normal computed using model hindcasts against CMAP-CPC. Each forecast case were to be issued on the 4th of November each year (1981- 2001). Document Reference: RES-LRF-AMESD-001-2010-09-13 Document Template Reference: TQM-PRE-001.1 Date of last revision: 26 May 2010

  24. SAWS experience in the area of climate modelling (operations and research) Extended-Range forecast: migration from subjective to objective probabilistic forecasts Document Reference: RES-LRF-AMESD-001-2010-09-13 Document Template Reference: TQM-PRE-001.1 Date of last revision: 26 May 2010

  25. SAWS experience in the area of climate modelling (operations and research) Toward a Coupled System (ECHAM4.5-MOM3) Courtesy of Magdalena Balmaseda ECMWF Document Reference: RES-LRF-AMESD-001-2010-09-13 Document Template Reference: TQM-PRE-001.1 Date of last revision: 26 May 2010

  26. SAWS experience in the area of climate modelling (operations and research) Toward a Coupled System (ECHAM4.5-MOM3) • Anomalously coupled system (lacking sea-ice model) • AGCM and OGCM are coupled using the multiple-program multiple-data (MPMD) paradigm. • Exchange information via data files every model simulation day. • No flux adjustment or empirical SST corrections is applied. ECHAM-MOM 2009/2010 summer season SST forecast Document Reference: RES-LRF-AMESD-001-2010-09-13 Document Template Reference: TQM-PRE-001.1 Date of last revision: 26 May 2010

  27. Further Readings • AMS Statement, 2001: Seasonal to interannual climate prediction (adopted by AMS Council 14 January 2001). Bull. Amer. Meteo. Soc., 82: 701-703, 710. • Goddard, L., S. J. Mason, S. E. Zebiak, C. F. Ropelewski, R, Basher, and M. A. Cane (2001) Current approaches to seasonal-to-interannual climate predictions. International Journal of Climatology, 21, 1111–1152. • Kalnay, E., S.J. Lord & R.D. McPherson, 1998: Maturity of Operational Numerical Weather Prediction: Medium Range, Bull. Amer. Meteo. Soc., 79, 2753-2769. • Shukla, J. (1981) Dynamical predictability of monthly means. J. Atmos.Sci. 38: 2547–2572. Document Reference: RES-LRF-AMESD-001-2010-09-13 Document Template Reference: TQM-PRE-001.1 Date of last revision: 26 May 2010

  28. PART 2 • SAWS MULITI-MODEL SYSTEM AND ITS INTERPRETATION Document Reference: RES-LRF-AMESD-001-2010-09-13 Document Template Reference: TQM-PRE-001.1 Date of last revision: 26 May 2010

  29. SAWS Operational Multi-Model System Current Multi-Model Setup • Current GCM’s in use • ECHAM4.5 – SAWS • CFS – NCEP • ECHAM4.5-MOM3 – IRI • Current Statistical Software used • Climate Predictability Tool (CPT) – IRI Document Reference: RES-LRF-AMESD-001-2010-09-13 Document Template Reference: TQM-PRE-001.1 Date of last revision: 26 May 2010

  30. Example of Multi-Model Seasonal Forecast • Currently only probability maps are produced • Forecasts for 3 rolling (monthly) 3-month seasons • Current variables include Total Precipitation, Minimum and Maximum Temperature • 0.5 Degree resolution from 5N-35S and 5E-52.5E • Various Formats can be provided Document Reference: RES-LRF-AMESD-001-2010-09-13 Document Template Reference: TQM-PRE-001.1 Date of last revision: 26 May 2010

  31. Example of Multi-Model Seasonal Forecast • Verification Maps for the First Season • Tercile Climate Maps for Each Season • Daily Rainfall Maps from 1971-2009 Document Reference: RES-LRF-AMESD-001-2010-09-13 Document Template Reference: TQM-PRE-001.1 Date of last revision: 26 May 2010

  32. Interpretation of Seasonal Forecasts – Certainty vs. Uncertainty Very Uncertain Forecast - Equal Chance for any of the three categories to occur AB=33-40 NN=20-33 BN=33-40 Document Reference: RES-LRF-AMESD-001-2010-09-13 Document Template Reference: TQM-PRE-001.1 Date of last revision: 26 May 2010

  33. Interpretation of Seasonal Forecasts – Certainty vs. Uncertainty Uncertain Forecast – Although there is a slight favor for the Below-Normal category to occur AB=33-40 NN=15-27 BN=40-45 Document Reference: RES-LRF-AMESD-001-2010-09-13 Document Template Reference: TQM-PRE-001.1 Date of last revision: 26 May 2010

  34. Interpretation of Seasonal Forecasts – Certainty vs. Uncertainty Certain Forecast – The Below-Normal category is heavily favored to occur AB=<33 NN=0-50 BN=>50 Document Reference: RES-LRF-AMESD-001-2010-09-13 Document Template Reference: TQM-PRE-001.1 Date of last revision: 26 May 2010

  35. Interpretation of Seasonal Forecasts – Skillful vs. Non-Skillful Uncertain Forecast – Although there is a slight favor for the Below-Normal category to occur and there is skill in predicting the Below-Normal Category AB=33-40 NN=15-27 BN=40-45 ROC= >0.5 Medium confidence for Below-Normal to occur Document Reference: RES-LRF-AMESD-001-2010-09-13 Document Template Reference: TQM-PRE-001.1 Date of last revision: 26 May 2010

  36. Confidence levels = + Low Probability High/Low Skill Low Confidence + = High Probability Low Skill Low Confidence + = Medium Probability High Skill Medium Confidence = + High Probability High Skill High Confidence Document Reference: RES-LRF-AMESD-001-2010-09-13 Document Template Reference: TQM-PRE-001.1 Date of last revision: 26 May 2010

  37. Further Readings • Landman, W. and A. Beraki: 2010: Multi-model forecast skill for mid-summer rainfall over southern Africa, accepted, International Journal of Climatology Document Reference: RES-LRF-AMESD-001-2010-09-13 Document Template Reference: TQM-PRE-001.1 Date of last revision: 26 May 2010

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